Session Commands

Minitab

Session Commands

Minitab

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, Minitab Model Ops

, Minitab Engage

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Contents

Using Session Commands............................................................................................................................................11

Alphabetical list of session commands................................................................................................................................................11

What are session commands?.................................................................................................................................................................27

Session command syntax notation........................................................................................................................................................27

Symbols to use with session commands.............................................................................................................................................27

Using a subcommand.................................................................................................................................................................................28

Using session commands in the Command Line pane and the History pane................................................................... 28

Using the History pane..............................................................................................................................................................................29

Rules for entering session command arguments.............................................................................................................................29

Rules for entering session commands..................................................................................................................................................30

Interrupting session command execution...........................................................................................................................................30

Updates for release 19.1............................................................................................................................................................................30

Commands that are unavailable in the web app..............................................................................................................................36

Opening, Saving, and Printing Files...........................................................................................................................38

END: Session command for ending data input.................................................................................................................................38

GSAVE: Session subcommand for saving a graph in a file........................................................................................................... 38

ODBC: Session command for importing data from a database file.......................................................................................... 39

OUTFILE and NOOUTFILE: Session commands for saving a Minitab session in a text file.............................................. 40

PRINT: Session command for displaying columns, constants, or matrices in the output pane..................................... 40

READ data into columns............................................................................................................................................................................41

READ data into a matrix.............................................................................................................................................................................43

RESTART: Session command for restarting.........................................................................................................................................43

RETRIEVE: Session command for retrieving a saved worksheet or project............................................................................ 44

SAVE: Session command for saving a worksheet or project........................................................................................................44

STOP: Session command for closing Minitab....................................................................................................................................45

WOPEN: Session command for opening a worksheet................................................................................................................... 45

WORKSHEET: Session command for making a worksheet active, for closing a worksheet, or for renaming a worksheet

..............................................................................................................................................................................................................................48

WRITE: Session command for writing data to the screen or a data file.................................................................................. 49

WSAVE: Session command for saving a worksheet file................................................................................................................. 51

XPPOINT: Session command for sending output to Microsoft PowerPoint.......................................................................... 53

XWORD: Session command for sending output to Microsoft Word........................................................................................53

Other Session Commands............................................................................................................................................54

PYSC: Session command for running a Python script....................................................................................................................54

RSCR: Session command for running a R script...............................................................................................................................54

NAME: Session command for assigning names to columns, stored constants, and matrices....................................... 55

ABORT: Subcommand for exiting a multi-line command.............................................................................................................56

HELP: Session command for opening this guide.............................................................................................................................56

Dynamic Data Exchange..............................................................................................................................................57

XDACTIVATE: Session command for activating a link.....................................................................................................................57

XDADD: Session command for adding a new link........................................................................................................................... 57

XDDEACTIVATE: Session command for deactivating a client link.............................................................................................. 59

XDEXEC: Session command for executing a command in a remote application................................................................ 60

XDGET: Session command for performing a one-time data transfer....................................................................................... 60

XDREMOVE: Session command for deleting an established link............................................................................................... 61

Manipulating and Calculating Data...........................................................................................................................63

Calculator.........................................................................................................................................................................................................63

Data..................................................................................................................................................................................................................106

Editor...............................................................................................................................................................................................................132

Basic Statistics.............................................................................................................................................................139

DESCRIBE: Session command for summarizing numeric data with statistics..................................................................... 139

STATS: Session command for storing descriptive statistics....................................................................................................... 143

GSUMMARY: Session command for displaying a graphical summary of each variable................................................ 146

ONEZ: Session command for performing a 1-sample Z-test.................................................................................................... 147

ONET: Session command for performing a 1-sample t-test..................................................................................................... 148

TWOT: Session command for performing a 2-sample t-test when samples are in one column................................ 149

TWOSAMPLE: Session command for performing a 2-sample t-test when the samples are in different columns. 150

PAIR: Session command for performing a paired t-test............................................................................................................. 151

PONE: Session command for performing a hypothesis test of the proportion................................................................ 152

PTWO: Session command for performing a hypothesis test of the difference between two proportions............ 154

ONERATE: Session command for performing a 1-sample Poisson rate test....................................................................... 155

TWORATE: Session command for performing a 2-sample Poisson rate test...................................................................... 156

ONEV: Session command for performing a 1 variance test...................................................................................................... 157

TWOVARIANCES: Session command for determining whether the variances or standard deviations of two groups

differ................................................................................................................................................................................................................158

CORRELATION: Session command for measuring the strength and direction of the association between two

variables.........................................................................................................................................................................................................160

COVARIANCE: Session command for calculating the covariance between pairs of columns...................................... 162

NORMTEST: Session command for performing a normality test............................................................................................ 163

OUTLIER: Session command for performing an outlier test...................................................................................................... 165

PGOODNESS: Session command for performing a chi-square goodness-of-fit test for Poisson distribution...... 166

Regression...................................................................................................................................................................168

INDICATOR: Session command for creating indicator variables.............................................................................................. 168

REGRESS: Session command for performing a regression analysis....................................................................................... 169

BREG: Session command for performing best subsets regression.........................................................................................178

FITLINE: Session command for creating a fitted line plot.......................................................................................................... 179

SSWORKSHEET: Session command for creating a stability study worksheet..................................................................... 182

SHELFLIFE: Session command for performing a stability study............................................................................................... 183

NLINEAR: Session command for performing nonlinear regression.......................................................................................188

OREG: Session command for performing orthogonal regression.......................................................................................... 192

PLS: Session command for performing partial least squares regression............................................................................. 194

GZLM: Session command for fitting a binary logistic model or a Poisson model........................................................... 199

BFIT: Session command for creating a binary fitted line plot................................................................................................... 210

OLOGISTIC: Session command for performing ordinal logistic regression......................................................................... 213

Minitab Statistical Software Contents

NLOGISTIC: Session command for performing nominal logistic regression...................................................................... 216

ANOVA.........................................................................................................................................................................219

ONEWAY: Session command for performing a one-way ANOVA........................................................................................... 219

ANOM: Session command for creating an analysis of means chart...................................................................................... 223

ANOVA: Session command for performing a balanced ANOVA............................................................................................. 224

GLM: Session command for fitting the general linear model................................................................................................... 227

REML: Session command for fitting a mixed effects model...................................................................................................... 235

COMPARE: Session command for performing multiple comparisons of means............................................................... 241

MANOVA: Session command for performing a general MANOVA........................................................................................ 244

NESTED: Session command for performing a fully-nested ANOVA....................................................................................... 248

VARTEST: Session command for performing an equal variances test................................................................................... 249

INTPLOT: Session command for creating an interval plot.......................................................................................................... 250

MAIN: Session command for creating a main effects plot........................................................................................................253

INTERACT: Session command for creating an interactions plot.............................................................................................. 254

DOE..............................................................................................................................................................................257

Screening Designs......................................................................................................................................................................................257

Factorial Designs.........................................................................................................................................................................................277

Response Surface Designs......................................................................................................................................................................342

Mixture Designs..........................................................................................................................................................................................365

Taguchi Designs..........................................................................................................................................................................................412

Modify and Display Designs..................................................................................................................................................................420

Quality and Process Improvement...........................................................................................................................425

Quality Planning Tools..............................................................................................................................................................................425

Control Charts..............................................................................................................................................................................................432

Capability Analysis.....................................................................................................................................................................................593

Tolerance Intervals.....................................................................................................................................................................................662

Measurement Systems Analysis............................................................................................................................................................666

Acceptance Sampling...............................................................................................................................................................................688

Reliability/Survival.....................................................................................................................................................695

Test Plans.......................................................................................................................................................................................................695

Distribution Analysis (Right Censoring).............................................................................................................................................703

Distribution Analysis (Arbitrary Censoring)......................................................................................................................................718

Growth Curves.............................................................................................................................................................................................729

Regression with Life Data........................................................................................................................................................................735

Cox Regression............................................................................................................................................................................................740

Probit Analysis.............................................................................................................................................................................................750

Warranty Analysis.......................................................................................................................................................................................754

Predictive Analytics....................................................................................................................................................758

CART Regression.........................................................................................................................................................................................758

CART Classification....................................................................................................................................................................................765

TreeNet Regression....................................................................................................................................................................................773

TreeNet Classification................................................................................................................................................................................784

Minitab Statistical Software Contents

Random Forests Regression...................................................................................................................................................................796

Random Forests Classification...............................................................................................................................................................801

MARS Regression.......................................................................................................................................................................................807

Discover Best Model (Continuous Response).................................................................................................................................814

Discover Best Model (Binary Response)............................................................................................................................................827

Multivariate Analysis.................................................................................................................................................841

PCA: Session command for performing principal components analysis.............................................................................. 841

FACTOR: Session command for performing a factor analysis................................................................................................... 842

CLUOBS: Session command for clustering observations............................................................................................................845

CLUVARS: Session command for clustering variables..................................................................................................................849

KMEANS: Session command for non-hierarchical clustering of observations................................................................... 851

DISCRIMINANT: Session command for performing discriminant analysis.......................................................................... 852

ITEMANALYSIS: Session command for performing item analysis........................................................................................... 854

CA: Session command for performing simple correspondence analysis............................................................................. 855

MCA: Session command for performing a multiple correspondence analysis.................................................................. 859

Time Series Analysis...................................................................................................................................................862

AAMODEL: Session command for the selection of an alternative model from Forecast with Best ARIMA

Model.............................................................................................................................................................................................................862

ACF: Session command for calculating autocorrelation.............................................................................................................862

ADF: Session command for conducting an augmented Dickey-Fuller test......................................................................... 863

ARIMA: Session command for modeling time series behavior and generating forecasts............................................ 864

ATARIMA: Session command for selecting parameters for an ARIMA model and generating forecasts................ 866

CCF: Session command for calculating cross correlation between two time series........................................................ 871

DECOMP: Session command for performing decomposition on a time series................................................................. 872

DES: Session command for performing double exponential smoothing............................................................................. 876

DIFFERENCES: Session command for calculating differences...................................................................................................880

LAG: Session command for calculating the lags of a column................................................................................................... 881

MLAG: Session command to calculate lags of one or more columns................................................................................... 881

MA: Session command for calculating a moving average......................................................................................................... 882

PACF: Session command for calculating partial autocorrelation............................................................................................. 886

SES: Session command for performing single exponential smoothing................................................................................ 887

TBOXCOX: Session command for performing a Box-Cox transformation on time series data................................... 891

TREND: Session command for performing a trend analysis...................................................................................................... 892

TSWINT: Session command for performing Holt-Winters seasonal exponential smoothing...................................... 896

Tables...........................................................................................................................................................................901

TABLE: Session command for creating one-way, two-way, and multi-way tables using categorical variables...... 901

TALLY: Session command for displaying a one-way table for each column....................................................................... 903

TCHISQUARE: Session command for performing a chi-square goodness-of-fit test...................................................... 904

XTABS: Session command for displaying one-way, two-way, and multi-way tables for categorical variables...... 905

Nonparametric Analysis............................................................................................................................................909

1-Sample Sign..............................................................................................................................................................................................909

1-Sample Wilcoxon....................................................................................................................................................................................909

MANN-WHITNEY: Session command for performing a Mann-Whitney test..................................................................... 910

Minitab Statistical Software Contents

FRIEDMAN: Session command for performing a Friedman test............................................................................................. 910

KRUSKAL-WALLIS: Session command for performing a Kruskal-Wallis test....................................................................... 911

MOOD: Session command for performing a Mood's median test......................................................................................... 911

RUNS: Session command for performing a runs test.................................................................................................................. 911

WALSH: Session command for calculating pairwise averages.................................................................................................912

WDIFF: Session command for calculating pairwise differences...............................................................................................912

WSLOPE: Session command for calculating pairwise slopes....................................................................................................912

Equivalence Test.........................................................................................................................................................914

TOST: Session command for performing an equivalence test..................................................................................................914

Power and Sample Size Analysis..............................................................................................................................919

FDESIGN: Session subcommand for power and sample size for a general full factorial design................................. 919

FFDESIGN: Session subcommand for power and sample size for a 2-level factorial design....................................... 920

ONERATE: Session subcommand for power and sample size for a 1-sample Poisson rate test................................. 921

ONEVARIANCE: Session command for power and sample size for a 1 variance test..................................................... 923

ONEWAY: Session command for power and sample size for one-way ANOVA................................................................ 924

PBDESIGN: Session subcommand for power and sample size for Plackett-Burman design........................................ 925

PONE: Session subcommand for power and sample size for a 1 proportion test........................................................... 926

POWER: Session command for power and sample size.............................................................................................................. 927

PTWO: Session subcommand for power and sample size for a 2 proportion test.......................................................... 929

SSCI: Session command for estimating sample size.................................................................................................................... 930

SSTI: Session command for sample size for tolerance intervals.............................................................................................. 931

TONE: Session command for power and sample size for a 1-sample t-test....................................................................... 933

TOST: Session command for power and sample size for an equivalence test................................................................... 934

TPAIRED: Session subcommand for power and sample size for a paired t-test................................................................ 935

TTWO: Session subcommand for power and sample size for a 2-sample t-test.............................................................. 936

TWORATE: Session subcommand for power and sample size for a 2-sample Poisson rate test................................ 938

TWOVARIANCE: Session subcommand for power and sample size for a 2 variances test........................................... 939

ZONE: Session subcommand for power and sample size for a 1-sample Z-test.............................................................. 940

Graphs..........................................................................................................................................................................942

CHART: Session command for creating a bar chart......................................................................................................................942

HMAP: Session command for creating a heatmap....................................................................................................................... 945

PLOT: Session command for creating a scatterplot......................................................................................................................947

NPLOT: Session command for creating a binned scatterplot...................................................................................................949

GCORRELATION: Session command for creating a correlogram............................................................................................ 951

MATRIXPLOT: Session command for creating a matrix of plots............................................................................................... 952

BUBBLEPLOT: Session command for creating a bubble plot..................................................................................................... 955

MARGPLOT: Session command for creating a marginal plot.................................................................................................... 957

HISTOGRAM: Session command for creating a histogram........................................................................................................ 958

DOTPLOT: Session command for creating a dotplot.................................................................................................................... 961

STEM-AND-LEAF: Session command for creating a stem-and-leaf plot.............................................................................. 963

PPLOT: Session command for creating a probability plot.......................................................................................................... 963

ECDF: Session command for creating an empirical CDF plot................................................................................................... 968

DPLOT: Session command for creating a probability distribution plot................................................................................. 970

BOXPLOT: Session command for creating a boxplot....................................................................................................................975

Minitab Statistical Software Contents

INTPLOT: Session command for creating an interval plot.......................................................................................................... 978

INDPLOT: Session command for creating an individual value plot........................................................................................ 980

LPLOT: Session command for creating a line plot.........................................................................................................................983

PIECHART: Session command for creating a pie chart................................................................................................................ 985

TSPLOT: Session command for creating a time series plot........................................................................................................ 987

PARPLOT: Session command for creating a parallel plot............................................................................................................ 989

ARGRAPH: Session command for creating an area graph......................................................................................................... 990

CONTOURPLOT: Session command for creating a contour plot............................................................................................. 992

PLTX: Session command for creating a 3D scatterplot................................................................................................................995

SURFACEPLOT: Session command for creating a surface plot................................................................................................. 996

Graph Options.............................................................................................................................................................................................999

Model-Based Commands........................................................................................................................................1094

FACPLOT: Session command for creating a factorial plot........................................................................................................ 1094

MFFCUBE: Session command for creating a cube plot for fitted means........................................................................... 1095

MMOPT: Session command for the Response Optimizer........................................................................................................ 1095

MOVERCONT: Session command for creating an overlaid contour plot........................................................................... 1098

MSURFACE: Session command for creating a surface plot..................................................................................................... 1099

PREDICT: Session command for predicting response values................................................................................................. 1101

MMPREDICT: Session command to calculate predictions from multiple models.......................................................... 1105

RMCONTOUR: Session command for creating a contour plot.............................................................................................. 1107

Macros Session Commands.....................................................................................................................................1109

Structure Commands..............................................................................................................................................................................1109

Declaration Statements.........................................................................................................................................................................1109

Local Macro Variables.............................................................................................................................................................................1112

Control Statements..................................................................................................................................................................................1115

Using DOS Commands..........................................................................................................................................................................1121

Labeling Macro Output.........................................................................................................................................................................1121

Debugging Tools......................................................................................................................................................................................1123

Additional Local Macro Features.......................................................................................................................................................1124

Commands that Affect Output...........................................................................................................................................................1125

Communicating with Macro Users....................................................................................................................................................1128

Execs Commands.....................................................................................................................................................................................1129

Supporting Concepts...............................................................................................................................................1131

Add your own function..........................................................................................................................................................................1131

Adding comments to a macro............................................................................................................................................................1131

Array table for OADESIGN....................................................................................................................................................................1131

Assigning attributes to groups...........................................................................................................................................................1132

Assigning attributes with multiple graphs and groups.............................................................................................................1132

Bar chart functions..................................................................................................................................................................................1133

Base position for project lines, area, and bar................................................................................................................................1134

Box-Behnken designs.............................................................................................................................................................................1135

Calculating a chi-square statistic for a goodness-of-fit test using session commands............................................... 1135

Calculations for FFACTORIAL...............................................................................................................................................................1136

Central composite designs...................................................................................................................................................................1136

Minitab Statistical Software Contents

Computing weights, or smoothed values......................................................................................................................................1138

Data requirements and formats for attribute agreement analysis....................................................................................... 1138

Default date/time formats....................................................................................................................................................................1140

Designs generated by FFDESIGN.......................................................................................................................................................1141

Entering data for factor variables......................................................................................................................................................1142

Entering data for response variables................................................................................................................................................1142

Entering patterned data for the SET session command........................................................................................................... 1143

Restricted and unrestricted mixed models....................................................................................................................................1144

Examples of entering response data for logistic regression................................................................................................... 1144

A comparison of MSURFACE and RMCONTOUR plots............................................................................................................. 1148

Graphics options for MIXCONTOUR.................................................................................................................................................1148

Graphics options for MIXOVER..........................................................................................................................................................1150

Graphics options for MIXSURFACE...................................................................................................................................................1150

Graphics options for MSURFACE.......................................................................................................................................................1152

Graphics options for SIMPLEX............................................................................................................................................................1154

Graphics options for SPCONT.............................................................................................................................................................1155

Graphics options for SPSIMP..............................................................................................................................................................1157

Graphs that use groups with the data display subcommands ............................................................................................. 1158

How to enter data for ANOVA and GLM........................................................................................................................................1158

How to enter data for CCHART..........................................................................................................................................................1159

How to enter data for GCHART..........................................................................................................................................................1159

How to enter data for NPCHART.......................................................................................................................................................1160

How to enter data for PDIAGNOSTIC, PCHART, and PPRIMECHART.................................................................................. 1161

How to enter data for TCHART...........................................................................................................................................................1161

How to enter data for UDIAGNOSTIC, UCHART, and UPRIMECHART................................................................................. 1163

How to enter subgroup data...............................................................................................................................................................1163

How to specify the model for ATCLASS, GZLM, OLOGISTIC and NLOGISTIC.................................................................. 1165

How to specify the model for factorial designs...........................................................................................................................1166

How to specify the model for GLM...................................................................................................................................................1167

How to specify the model for response surface designs.........................................................................................................1168

How to specify the model in ANOVA...............................................................................................................................................1169

How to specify the model in MGAGE..............................................................................................................................................1170

Missing values in exponential smoothing......................................................................................................................................1171

Missing values in factorial, response surface, and mixture designs..................................................................................... 1171

Notes on subcommands that store descriptive statistics (STATS command).................................................................. 1172

Numbers for colors to use in session commands.......................................................................................................................1172

Numbers for fill types to use in session commands..................................................................................................................1173

Numbers for line types to use in session commands................................................................................................................1173

Numbers to use for symbols and markers in session commands........................................................................................ 1174

Overview of DDE session commands..............................................................................................................................................1175

Pearson residuals.....................................................................................................................................................................................1175

Plackett-Burman designs......................................................................................................................................................................1176

Prompting a user for information.....................................................................................................................................................1177

Reading in data from a text file..........................................................................................................................................................1177

Generalized linear model diagnostics and residual analysis................................................................................................... 1177

Residual analysis and regression diagnostics...............................................................................................................................1178

Minitab Statistical Software Contents

Restrictions on GLM models................................................................................................................................................................1179

Session commands that are not allowed in macros...................................................................................................................1179

Simplex lattice design descriptions..................................................................................................................................................1180

Time unit subcommands......................................................................................................................................................................1180

Using groups in graphs.........................................................................................................................................................................1182

Using READ with FORMAT...................................................................................................................................................................1183

Using READ without subcommands.................................................................................................................................................1183

Using the SYMBOL and COLOR subcommands for PLOT........................................................................................................ 1183

Using the POSITION and MODEL subcommands.......................................................................................................................1185

Valid format items....................................................................................................................................................................................1186

Minitab Statistical Software Contents

Using Session Commands

Alphabetical list of session commands

AASA: Session command for acceptance sampling by attributes on page 688

ACF: Session command for calculating autocorrelation on page 862

ADD: Session command for addition on page 63

ALTTESTPLAN: Session command for creating an accelerated life test plan on page 699

ANOM: Session command for creating an analysis of means chart on page 223

ANOVA: Session command for performing a balanced ANOVA on page 224

ARDECISION: Session command for accepting or rejecting an entire lot on page 694

AREA: The session subcommand for shading the area below the data values to the base on page 999

ARGRAPH: Session command for creating an area graph on page 990

ARIMA: Session command for modeling time series behavior and generating forecasts on page 864

AXLABEL: Session subcommand for customizing graph axis labels on page 1001

BAR: Session subcommand for representing data values with bars on page 1003

BASE: Session command for fixing a starting number for the random number generator on page 86

BBDESIGN: Session command for creating a Box-Behnken design on page 342

BCAPA: Session command for performing binomial capability analysis on page 656

BFFACTORIAL: Session command for analyzing a full or fractional factorial design with a binary response on page

296

BFIT: Session command for creating a binary fitted line plot on page 210

BGFACTORIAL: Session command for analyzing a general full factorial design with a binary response. on page 312

BOXCOX: Session command for performing a Box-Cox transformation on page 432

BOXPLOT: Session command for creating a boxplot on page 975

BTFT: Session command for calculating a 1-sample bootstrap confidence interval of a function on page 96

BTPR: Session command for calculating a 1-sample bootstrap confidence interval of a proportion on page 97

BTTM: Session command for calculating a 2-sample bootstrap confidence interval for the difference of means on

page 99

BREAK: Session command for transferring control from a DO- or WHILE-loop on page 1118

BREG: Session command for performing best subsets regression on page 178

Minitab Statistical Software Using Session Commands

BRIEF: Session command for controlling the amount of output on page 1125

BUBBLEPLOT: Session command for creating a bubble plot on page 955

BRSREG: Session command for analyzing a response surface design with a binary response on page 353

BSCREEN: Session command for analyzing a screening design with a binary response on page 268

BWCAPA: Session command for performing between/within capability analysis on page 596

BWCHART: Session command for creating an I-MR-R/S chart on page 446

BWSIXPAC: Session command for Between/Within Capability Sixpack on page 641

CA: Session command for performing simple correspondence analysis on page 855

CALL and RETURN: Session commands for passing control to another macro on page 1119

CAPA: Session command for performing a normal capability analysis on page 604

CCDESIGN: Session command for creating a central composite design on page 343

CCF: Session command for calculating cross correlation between two time series on page 871

CCHART: Session command for creating a C chart on page 531

CD: Session command for displaying or changing the current directory on page 1121

CDF: Session command for calculating the cumulative probability of an x-value on page 91

CENTER: Session command for centering data on page 80

CFAUTOMATICALLY: Session command for automatically recalculating values on page 135

CFMANUALLY: Session command for manually recalculating values on page 136

CFNOW: Session command for recalculating values now on page 136

CFORMAT: Session command for conditional formatting of worksheet cells on page 132

CHART: Session command for creating a bar chart on page 942

CIBOX: The session subcommand for displaying a median confidence interval box on a boxplot on page 1005

CLIMITS: Session command for specifying attributes for control limit lines on page 1006

CLINE: Session command for specifying attributes for a center line on page 1008

CLUOBS: Session command for clustering observations on page 845

CLUVARS: Session command for clustering variables on page 849

CMEAN: Session subcommand for connecting means with lines on a boxplot on page 1010

CMEDIAN: Session subcommand for connecting means with lines on a boxplot on page 1011

CODE: Session command for changing values in columns to new values on page 119

COFFSET and GAPWIDTH: Session subcommands for the space between clusters and items in a cluster on page 1028

COMPARE: Session command for performing multiple comparisons of means on page 241

CONCATENATE: Session command for combining text columns on page 119

CONNECT: Session subcommand for connecting points with lines on page 1012

Minitab Statistical Software Using Session Commands

CONTOURPLOT: Session command for creating a contour plot on page 992

%CONTPROC: Session command for creating a contour plot

CONVERT: Session command for converting text data to numeric data, and numeric data to text data on page 121

COPY: Session command for copying data on page 130

CORRELATION: Session command for measuring the strength and direction of the association between two variables

on page 160

COUNT: Session command for counting the number of values in a column on page 76

COVARIANCE: Session command for calculating the covariance between pairs of columns on page 162

CTPREDICT: Session command for predicting responses for new observations for a classification tree on page 771

CTREE: Session command for creating a classification tree on page 765

CUSUM: Session command for creating a CUSUM chart on page 550

CUTPOINT, MIDPOINT, and NINTERVAL: Session subcommands for specifying cutpoints and midpoints on page

1014

DATA: Session subcommand for controlling the data region within the figure region on page 1015

DATE: Session command for changing data type to date/time on page 113

DATLAB: Session command for labeling data values on page 1016

DCAPA: Session command for performing individual distribution identification on page 593

DEBUG and NODEBUG: Session commands for finding problems in macros on page 1124

DECOMP: Session command for performing decomposition on a time series on page 872

DEFAULT: Session command for assigning default values to subcommand arguments on page 1112

DEFINE: Session command for defining a constant matrix on page 104

DEFTEST: Session command for defining the sensitivity of the tests for special causes on page 592

DELETE: Session command for deleting rows of data on page 111

DES: Session command for performing double exponential smoothing on page 876

DESCRIBE: Session command for summarizing numeric data with statistics on page 139

DIAGONAL: Session command for creating a matrix from a column on page 104

DIFFERENCES: Session command for calculating differences on page 880

DISCRIMINANT: Session command for performing discriminant analysis on page 852

DISTRIBUTION: Session command for fitting a distribution on page 1018

DOT: Session subcommand for displaying a symbol for each data value on page 1021

DOTPLOT: Session command for creating a dotplot on page 961

DPLOT: Session command for creating a probability distribution plot on page 970

DSDESIGN: Session command for creating a definitive screening design on page 257

Minitab Statistical Software Using Session Commands

DROUND: Session command for rounding date/time values on page 122

DSET: Session command for making patterned data on page 82

DTESTPLAN: Session command for creating a demonstration test plan on page 695

DTYPE: Session command for determining the data type of a column or a constant on page 1114

ECDF: Session command for creating an empirical CDF plot on page 968

EIGEN: Session command for calculating eigenvalues on page 63

ELLIPSE: Session subcommand for constructing an ellipse from points on a graph on page 1022

ELSE, ELSEIF, IF, ENDIF: Session commands for executing code depending on a logical condition on page 1115

END: Session command for ending data input on page 38

ENDLAYOUT and LAYOUT: Session subcommands for specifying where a graph appears on a page on page 1043

ENDMACRO, GMACRO, and MACRO: Session commands for marking the beginning and ending of a macro on

page 1109

ENDWHILE and WHILE: Session commands for repeating a block of commands depending on a logical expression

on page 1116

ERASE: Session command for erasing variables on page 113

ETESTPLAN: Session command for creating an estimation test plan on page 697

EVDESIGN: Session command for creating an extreme vertices design on page 365

EWMACHART: Session command for creating an EWMA chart on page 543

EXCLUDE and INCLUDE: Session subcommands for including or excluding rows on a graph on page 1036

EXECUTE: Session command for running an Exec file on page 1130

EXIT: Session command for transferring control back to Minitab or for closing Minitab on page 1120

FACPLOT: Session command for creating a factorial plot on page 276

FACTOR: Session command for performing a factor analysis on page 842

FDATE/TIME: Session command for changing the format of date/time columns on page 136

FDESIGN: Session command for creating a general full factorial design on page 277

FDESIGN: Session subcommand for power and sample size for a general full factorial design on page 919

FFACTORIAL: Session command for analyzing a full or fractional factorial design on page 288

FFCUBE: Session command for creating a cube plot on page 340

FFDESIGN: Session command for creating a full or fractional factorial design on page 279

FFDESIGN: Session subcommand for power and sample size for a 2-level factorial design on page 920

FFINT: Superseded by FACPLOT except for mixture designs on page 336

FFMAIN: Superseded by FACPLOT except for mixture designs on page 338

Minitab Statistical Software Using Session Commands

FIGURE: Session subcommand for controlling the figure region within the graph region on page 1023

FISHBONE: Session command for creating a cause-and-effect diagram on page 425

FITD: Session command for fitting a distribution to the data on a probability plot on page 1024

FITLINE: Session command for creating a fitted line plot on page 179

FNUMERIC: Session command for changing columns to numeric format on page 136

FOOTNOTE: Session subcommand for adding a footnote to a graph on page 1025

%FORM: Session command for creating a data collection form for a 3-factor design

FORMULA: Session command for assigning a formula to a column on page 137

FREQUENCY: Session subcommand for using a frequency column for a graph on page 1027

FRIEDMAN: Session command for performing a Friedman test on page 910

FTEXT: Session command for changing the format of text columns on page 137

GAGERR: Session command for performing a crossed gage R&R study on page 675

GAPS: Session subcommand for displaying a gap in time on a graph on page 1027

GAPWIDTH and COFFSET: Session subcommands for the space between clusters and items in a cluster on page 1028

GAWORKSHEET: Session command for creating a gage R&R study worksheet on page 667

GCHART: Session command for creating a G chart on page 582

GENVAR: Session command for creating a generalized variance chart on page 572

GFACTORIAL: Session command for fitting a general full factorial design on page 305

GLM: Session command for fitting the general linear model on page 227

GMACRO, MACRO, and ENDMACRO: Session commands for marking the beginning and ending of a macro on

page 1109

GOTO and MLABEL: Session commands for branching to any line in a macro on page 1118

GRAPH: Session subcommand for controlling the graph region fill and border line on page 1028

MGRID, NOGRID, and NOMGRID: Session subcommands for controlling the grid on a graph on page 1029

GROUP: Session subcommand for specifying categorical variables for grouping on page 1029

GSAVE: Session subcommand for saving a graph in a file on page 38

GSCALE: Session command to determine appropriate scaling for a graph on page 1127

GSUMMARY: Session command for displaying a graphical summary of each variable on page 146

GZLM: Session command for fitting a binary logistic model or a Poisson model on page 199

HISTOGRAM: Session command for creating a histogram on page 958

HLABEL: Session subcommand for labeling histogram bars on a marginal plot with y-axis values on page 1030

Minitab Statistical Software Using Session Commands

HLINE: Session subcommand for specifying attributes for historical stage lines on page 1032

ICHART: Session command for creating an I chart on page 490

IDIDENTIFICATION: Session command for creating probability plots of arbitrarily-censored, failure (or survival) data

on page 718

IDOVIEW: Session command for creating a layout of distribution plots on page 720

IF, ELSEIF, ELSE, ENDIF: Session commands for executing code depending on a logical condition on page 1115

ILABEL: Session subcommand for labeling individual values on a boxplot or interval plot on page 1033

IMRCHART: Session command for creating an I-MR chart on page 479

INCLUDE and EXCLUDE: Session subcommands for including or excluding rows on a graph on page 1036

INDICATOR: Session command for creating indicator variables on page 85

INDIVIDUAL: Session subcommand for displaying a symbol for each individual data value on a boxplot or an

individual value plot on page 1036

INDPLOT: Session command for creating an individual value plot on page 980

INFO: Session command for summarizing the current worksheet on page 106

INTBAR: Session subcommand for displaying a vertical line with horizontal lines at the endpoints of the confidence

interval for the mean on page 1037

INTERACT: Session command for creating an interactions plot on page 254

INTLAB: Session subcommand for labeling interval bar endpoints on an interval plot on page 1039

INTPLOT: Session command for creating an interval plot on page 250

INVCDF: Session command for calculating the variable for a cumulative probability on page 94

INVERT: Session command for replacing a matrix value with its inverse on page 105

IQRBOX: Session subcommand for displaying an interquartile range box on a boxplot on page 1041

ITEMANALYSIS: Session command for performing item analysis on page 854

JITTER and NOJITTER: Session subcommands for randomly offsetting data points to reveal overlapping points on

page 1043

JOHNSON: Session command for applying the Johnson transformation on page 596

KKCAT, KKNAME, and KKSET: Session commands for using text on page 1112

KMEANS: Session command for non-hierarchical clustering of observations on page 851

KRUSKAL-WALLIS: Session command for performing a Kruskal-Wallis test on page 911

Minitab Statistical Software Using Session Commands

LAG: Session command for calculating the lags of a column on page 63

LAYOUT and ENDLAYOUT: Session subcommands for specifying where a graph appears on a page on page 1043

LBRIGHT: Session subcommand for specifying the brightness of the lights that illuminate a surface plot on page

1044

LEGEND and NOLEGEND: Session subcommands for controlling the legend on a graph on page 1045

LET: Session command for correcting a number in a worksheet or performing arithmetic on page 63

LIGHT: Session subcommand for specifying the position, color, and visibility of the lights that illuminate a surface

plot on page 1047

LINE: The session subcommand for constructing a line from points on a graph on page 1048

LNGAGE: Session command for performing a gage linearity and bias study on page 673

LONGMETHOD: Session command for performing an attribute gage study (analytic method) on page 684

LOWESS: Session subcommand for fitting a LOWESS smoother to a scatterplot, a matrix plot, a histogram, or a time

series plot on page 1049

LPLOT: Session command for creating a line plot on page 983

LREGRESSION: Session command for performing a regression analysis when the error distribution is Weibull, smallest

extreme value, exponential, log-normal, normal, logistic, or log-logistic on page 735

LTABLE: Session command for fitting a distribution to arbitrarily-censored data on page 722

LTEST: Session command for parametric or nonparametric distribution analysis on page 709

MA: Session command for calculating a moving average on page 882

MACHART: Session command for creating a moving average chart on page 536

MACRO, ENDMACRO, and GMACRO: Session commands for marking the beginning and ending of a macro on

page 1109

MAIN: Session command for creating a main effects plot on page 253

MANN-WHITNEY: Session command for performing a Mann-Whitney test on page 910

MANOVA: Session command for performing a general MANOVA on page 244

MARGPLOT: Session command for creating a marginal plot on page 957

MARKER: Session subcommand for displaying a symbol at specified points on a graph on page 1050

MATRIXPLOT: Session command for creating a matrix of plots on page 952

MCA: Session command for performing a multiple correspondence analysis on page 859

MCAPA: Session command for performing a normal capability analysis for multiple variables on page 626

MCONSTANT, MCOLUMN, MMATRIX, and MTYPE: Session commands for declaring variables on page 1109

MDESIGN: Session command to modify the design properties or display of a design in the worksheet on page 420

MEALAB: Session subcommand for labeling means on a boxplot or an interval plot on page 1051

MEAN: Session command for calculating the arithmetic mean of a column on page 76

Minitab Statistical Software Using Session Commands

MEAN: Session subcommand for displaying symbol for each mean on a boxplot, interval plot, or individual value

plot on page 1053

MEDIAN: Session command for identifying the median of a column on page 76

MEDIAN: Session subcommand for displaying a symbol for each median on a boxplot, interval plot, or individual

value plot on page 1054

MEDLAB: Session subcommand for labeling medians on a boxplot on page 1055

MERGE: Session command for merging two worksheets into one worksheet on page 106

MESH: Session command for making mesh data on page 84

MEWMA: Session command for creating a multivariate EWMA chart on page 577

MFFCUBE: Session command for creating a cube plot for fitted means on page 335

MFREE: Session command for declaring a free variable on page 1110

MGAGE: Session command for performing an expanded gage R&R study on page 670

MGRID, GRID, NOGRID, and NOMGRID: Session subcommands for controlling the grid on a graph on page 1029

MIDPOINT, CUTPOINT, and NINTERVAL: Session subcommands for specifying cutpoints and midpoints on page

1014

MIXCONTOUR: Session command for creating a contour plot on page 391

MIXOVER: Session command for creating an overlaid contour plot on page 399

MIXREG: Session command for analyzing a mixture design on page 375

MIXSURFACE: Session command for creating a surface plot on page 397

MLABEL and GOTO: Session commands for branching to any line in a macro on page 1118

MLAG: Session command to calculate lags of one or more columns on page 881

MMATRIX, MCONSTANT, MCOLUMN, and MTYPE: Session commands for declaring variables on page 1109

MMOPT: Session command for the Response Optimizer on page 1095

MNCAPA: Session command for performing nonnormal capability analysis for multiple variables on page 636

MOOD: Session command for performing a Mood's median test on page 911

MOVERCONT: Session command for creating an overlaid contour plot on page 1098

MRCHART: Session command for creating an MR chart on page 497

MROPT: Session command for the Response Optimizer on page 409

MSURFACE: Session command for creating a surface plot on page 1099

MTITLE: Session command for adding a title above output on page 1121

MTYPE, MMATRIX, MCONSTANT, and MCOLUMN: Session commands for declaring variables on page 1109

MULTIPLY: Session command for multiplication on page 75

MVARCHART: Session command for creating a multi-vari chart on page 427

N: Session command for counting the nonmissing values in a column on page 76

Minitab Statistical Software Using Session Commands

NESTED: Session command for performing a fully-nested ANOVA on page 248

NEXT: Session command for transferring control from a loop to the beginning of the block on page 1117

NGROWTH: Session command for performing a nonparametric analysis using a nonparametric growth curve on

page 732

NINTERVAL, CUTPOINT, and MIDPOINT: Session subcommands for specifying cutpoints and midpoints on page

1014

NLINEAR: Session command for performing nonlinear regression on page 188

NLOGISTIC: Session command for performing nominal logistic regression on page 216

NMISS: Session command for counting the missing values in a column on page 76

NMVARCHART: Session command for creating variability charts on page 427

NNCAPA: Session command for performing a nonnormal capability analysis on page 612

NNSIXPACK: Session command for Nonnormal Capability Sixpack on page 651

NNTINTERVALS: Session command for calculating tolerance intervals on page 664

NOBRUSH: Session subcommand for disabling brushing on a graph on page 1128

NODEBUG and DEBUG: Session commands for finding problems in macros on page 1124

NODOTFOOTNOTE: Session subcommand for suppressing footnotes on a dotplot on page 1057

NODTITLE, NODSUBTITLE, and NODFOOTNOTE: Session subcommands for suppressing titles, subtitles, and footnotes

on a graph on page 1057

NOECHO and ECHO: Session commands for displaying Minitab commands in the output on page 1123

NOEMPTY and NOMISS: Session subcommands for excluding missing data from graphs on page 1057

NOFRAME: Session subcommand for suppressing lines and labels on a graph on page 1057

NOGRID, GRID, MGRID, and NOMGRID: Session subcommands for controlling the grid on a graph on page 1029

NOHLEGEND: Session subcommand for suppressing the legend of hold values on a graph on page 1058

NOJITTER and JITTER: Session subcommands for randomly offsetting data points to reveal overlapping points on

page 1043

NOLEGEND and LEGEND Session subcommands for controlling the legend on a graph on page 1045

NOMGRID GRID, MGRID, and NOGRID, and: Session subcommands for controlling the grid on a graph on page

1029

NOMISS and NOEMPTY: Session subcommands for excluding missing data from graphs on page 1057

NOOUTFILE and OUTFILE: Session commands for saving a Minitab session in a text file on page 40

NOPERFOOTNOTE: Session subcommand for suppressing My Footnote on page 1058

NOPROPORTIONAL and PROPORTIONAL: Session subcommands for making the boxes on a boxplot proportional

to the square root of the number of observations in the boxes on page 1066

NORMTEST: Session command for performing a normality test on page 163

NOSEPSUBTITLE: Session subcommand for subtitles on separate graphs on page 1058

NOTABLE and TABLE: Session subcommand for controlling the table within the figure region on page 1085

NOTE: Session command for adding comments that are displayed in the output on page 1129

Minitab Statistical Software Using Session Commands

NOTRANSPOSE and TRANSPOSE: Session subcommands for transposing the x- and y-axis on a graph on page 1091

NPCHART: Session command for creating a chart for the number of defectives on page 514

NTGAGE: Session command for performing a nested gage R&R study on page 680

NUMERIC: Session command for changing the data format of a date/time column or extracting date/time components

on page 123

OADESIGN: Session command for creating a Taguchi orthogonal array design on page 412

OAPREDICT: Session command for calculating predicted response values on page 418

ODBC: Session command for importing data from a database file on page 39

OLAB: Session subcommand for labeling outliers on a boxplot on page 1058

OLOGISTIC: Session command for performing ordinal logistic regression on page 213

ONERATE: Session command for performing a 1-sample Poisson rate test on page 155

ONERATE: Session subcommand for power and sample size for a 1-sample Poisson rate test on page 921

ONET: Session command for performing a 1-sample t-test on page 148

ONEV: Session command for performing a 1 variance test on page 157

ONEVARIANCE: Session command for power and sample size for a 1 variance test on page 923

ONEWAY: Session command for power and sample size for one-way ANOVA on page 924

ONEZ: Session command for performing a 1-sample Z-test on page 147

OPTDES: Session command for selecting an optimal design on page 361

OREG: Session command for performing orthogonal regression on page 192

OUTFILE and NOOUTFILE: Session commands for saving a Minitab session in a text file on page 40

OUTLIER: Session command for performing an outlier test on page 165

OUTLIER: Session subcommand for displaying a symbol for each outlier on a boxplot on page 1060

OVERLAY: Session subcommand for combining graphs specified in a multiple graph command into a single graph

on page 1061

PACF: Session command for calculating partial autocorrelation on page 886

PAIR: Session command for performing a paired t-test on page 151

PANEL: Session subcommand for paneling graphs on page 1061

PARETO: Session command for creating a Pareto chart on page 428

PAUSE and RESUME: Session commands for pausing and resuming a macro on page 1121

PBDESIGN: Session command for creating a Plackett-Burman design on page 258

PBDESIGN: Session subcommand for power and sample size for Plackett-Burman design on page 925

Minitab Statistical Software Using Session Commands

PCA: Session command for performing principal components analysis on page 841

PCAPA: Session command for performing a Poisson capability analysis on page 659

PCHART: Session command for creating a P chart on page 503

PDF: Session command for calculating the probability distribution of a continuous random variable on page 89

PDIAGNOSTIC: Session command for determining whether to use a P chart or a Laney P' chart on page 502

PGOODNESS: Session command for performing a chi-square goodness-of-fit test for Poisson distribution on page

166

PGROWTH: Session command for performing a parametric analysis using a parametric growth curve on page 729

PIECHART: Session command for creating a pie chart on page 985

PLOT: Session command for creating a scatterplot on page 947

PLS: Session command for performing partial least squares regression on page 194

PLTX: Session command for creating a 3D scatterplot on page 995

POLYGON: Session subcommand for constructing a polygon from points on a graph on page 1063

PONE: Session command for performing a hypothesis test of the proportion on page 152

PONE: Session subcommand for power and sample size for a 1 proportion test on page 926

POWER: Session command for power and sample size on page 927

PPLOT: Session command for creating a probability plot on page 963

PPRIMECHART: Session command for creating a Laney P' chart on page 509

PREDICT: Session command for predicting response values on page 1101

PRINT: Session command for displaying columns, constants, or matrices in the output pane on page 40

PROBIT: Session command for performing probit analysis on page 750

PROJECT: Session subcommand for extending projection lines from each point to the x-axis on page 1064

PROPORTIONAL and NOPROPORTIONAL: Session subcommands for making the boxes on a boxplot proportional

to the square root of the number of observations in the boxes on page 1066

PTILES: Session subcommand for specifying the location of the percentile lines on a graph on page 1066

PTWO: Session command for performing a hypothesis test of the difference between two proportions on page 154

PTWO: Session subcommand for power and sample size for a 2 proportion test on page 929

PYSC: Session command for running a Python script on page 54

RANDOM: Session command for generating random data on page 87

RANGE: Session command for calculating a range of values in a column on page 77

RANK: Session command for ranking values in a column on page 110

RBOX: Session subcommand for displaying a range box on a boxplot on page 1070

RCHART: Session command for creating an R chart on page 461

Minitab Statistical Software Using Session Commands

RCOUNT: Session command for counting missing and nonmissing values in a row on page 77

RDIDENTIFICATION: Session command for creating a distribution ID plot on page 703

RDOVIEW: Session command for creating a layout of distribution plots on page 706

READ data into columns on page 41

READ data into a matrix on page 43

READ, TSET, and SET: Session command for asking users questions and using the answers in a macro on page 1128

RECTANGLE: Session subcommand for constructing a rectangle from points on a graph on page 1072

REFERENCE: Session subcommand for specifying the axis and location of reference lines on a graph on page 1073

REGRESS: Session command for performing a regression analysis on page 169

REGRESS: Session subcommand for fitting a regression line to data on a graph on page 1075

RESTART: Session command for restarting on page 43

RESUME and PAUSE: Session commands for pausing and resuming a macro on page 1121

RETRIEVE: Session command for retrieving a saved worksheet or project on page 44

RETURN and CALL: Session commands for passing control to another macro on page 1119

RFORMULA: Session command for removing formulas on page 137

RMAXIMUM: Session command for identifying the maximum value in each row on page 77

RMCONTOUR: Session command for creating a contour plot on page 1107

RMEAN: Session command for calculating the arithmetic mean in each row on page 78

RMEDIAN: Session command for identifying the median in each row on page 78

RMERGE: Session command for merging worksheets on page 131

RMINIMUM: Session command for identifying the minimum value in each row on page 78

RN: The session command for counting the nonmissing values in a row on page 78

RNGAGE: Session command for creating gage run chart on page 669

RNMISS: Session command for counting the missing values in a row on page 78

RNMN: Session command for performing a 1-sample randomization test of a mean on page 100

RNPR: Session command for performing a 1-sample randomization test of a proportion on page 101

RNTM: Session command for performing a 2-sample randomization test of means on page 102

ROBUST: Session command for analyzing a Taguchi design on page 414

ROWTOC: Session command for stacking multiple columns into one column on page 113

RRANGE: Session command for calculating the range in each row on page 79

RSCR: Session command for running a R script on page 54

RSREG: Session command for analyzing a response surface design with least squares regression on page 346

RSSQ: Session command for calculating the uncorrected sum of squares on page 79

RSTDEV: Session command for calculating the standard deviation in each row on page 79

Minitab Statistical Software Using Session Commands

RSUM: Session command for adding the values in each row on page 79

RTPREDICT: Session command for predicting responses for new observations for a regression tree on page 763

RTREE: Session command for creating a regression tree on page 758

RUNCHART: Session command for creating a run chart on page 430

RUNS: Session command for performing a runs test on page 911

SAME: Session subcommand for specifying that one or more axes are the same for multiple graphs on page 1076

SAMPLE: Session command for generating rows of random data from specified columns on page 89

SAVE: Session command for saving a worksheet or project on page 44

SCALE: Session subcommand for customizing the axes and ticks of a graph on page 1076

SCDESIGN: Session command for creating a simplex centroid design on page 369

SCHART: Session command for creating an S chart on page 467

SCREEN: Session command for analyzing a screening design on page 260

SEPARATE: Session subcommand for placing groups in separate graphs when you use a paneling variable on page

1079

SES: Session command for performing single exponential smoothing on page 887

SET: Session command for entering data into a column on page 80

SFIT: Session subcommand for specifying the attributes of the surface on a 3D surface plot on page 1079

SHELFLIFE: Session command for performing a stability study on page 183

SIMPLEX: Session command for creating a simplex design plot on page 383

%SIMPROC: Session command for creating a simplex plot on page 388

SINTERVAL: Session command for calculating a sign confidence interval on page 909

SIXPACK: Session command for Normal Capability Sixpack on page 646

SLABEL: Session subcommand for labeling the slices on a pie chart on page 1080

SLDESIGN: Session command for creating a mixture design on page 372

SLICE: Session subcommand for specifying the attributes of slices on a pie chart on page 1081

SOFFSET: Session subcommand for offsetting points from the center on page 1082

SORT: Session command for sorting columns on page 108

SPCONT: Session command for creating a contour design plot on page 394

SPDESIGN: Session command for creating a 2-level split-plot design on page 284

SPFACTORIAL: Session command for analyzing a 2-level split-plot design on page 321

SPLIT: Session command for splitting a worksheet into multiple worksheets on page 112

SPSIMP: Session command for creating a simplex design plot on page 385

SSCI: Session command for estimating sample size on page 930

Minitab Statistical Software Using Session Commands

SSQ: Session command for calculating the uncorrected sum of squares on page 77

SSTI: Session command for sample size for tolerance intervals on page 931

SSWORKSHEET: Session command for creating a stability study worksheet on page 182

STACK: Session command for stacking blocks of columns and constants on top of each other on page 126

STAMP: Session subcommand for specifying columns that contain time values for observations on page 1083

STATS: Session command for storing descriptive statistics on page 143

STDEV: Session command for calculating the standard deviation of all the values in a column on page 77

STEM-AND-LEAF: Session command for creating a stem-and-leaf plot on page 963

STEST: Session command for performing a 1-sample sign test on page 909

STOP: Session command for closing Minitab on page 45

SUBSET: Session command for copying specified rows to a new worksheet on page 114

SUBTITLE: Session subcommand for adding a subtitle to a graph on page 1083

SUM: Session command for adding the values in a column on page 77

SURFACEPLOT: Session command for creating a surface plot on page 996

SYMBOL: Session subcommand for displaying a symbol for each data value on page 1084

SYMPLOT: Session command for creating a symmetry plot on page 432

TABLE and NOTABLE: Session subcommand for controlling the table within the figure region on page 1085

TABLE: Session command for creating one-way, two-way, and multi-way tables using categorical variables on page

901

TALLY: Session command for displaying a one-way table for each column on page 903

TCHART: Session command for creating a T chart on page 587

TCHISQUARE: Session command for performing a chi-square goodness-of-fit test on page 904

TEXT: Session command for changing the data type of a column to text on page 124

TEXT: Session subcommand for displaying text on a graph on page 1088

TITLE: Session subcommand for adding a title to a graph on page 1089

TOGAGE: Session command for performing a Type 1 gage study on page 666

TOLINTERVALS: Session command for calculating tolerance intervals on page 662

TONE: Session command for power and sample size for a 1-sample t-test on page 933

TOST: Session command for performing an equivalence test on page 914

TOST: Session command for power and sample size for an equivalence test on page 934

TPAIRED: Session subcommand for power and sample size for a paired t-test on page 935

TRACE: Session command for creating a response trace plot on page 389

TRANSPOSE and NOTRANSPOSE: Session subcommands for transposing the x- and y-axis on a graph on page 1091

Minitab Statistical Software Using Session Commands

TRANSPOSE: Session command for changing rows to columns, and columns to rows on page 105

TREND: Session command for performing a trend analysis on page 892

TSET: Session command for creating data that follow complicated patterns on page 81

TSGV: Session command for creating a Tsquared-generalized variance chart on page 562

TSHOW: Session subcommand for specifying the level of tick labels that are displayed on page 1091

TSPLOT: Session command for creating a time series plot on page 987

TSQUARED: Session command for creating a Tsquared chart on page 567

TSWINT: Session command for performing Holt-Winters seasonal exponential smoothing on page 896

TTWO: Session subcommand for power and sample size for a 2-sample t-test on page 936

TWORATE: Session command for performing a 2-sample Poisson rate test on page 156

TWORATE: Session subcommand for power and sample size for a 2-sample Poisson rate test on page 938

TWOSAMPLE: Session command for performing a 2-sample t-test when the samples are in different columns on

page 150

TWOT: Session command for performing a 2-sample t-test when samples are in one column on page 149

TWOVARIANCES: Session command for determining whether the variances or standard deviations of two groups

differ on page 158

TWOVARIANCE: Session subcommand for power and sample size for a 2 variances test on page 939

UCHART: Session command for creating a U chart on page 525

UDIAGNOSTIC: Session command for determining whether to use a U chart or a Laney U' chart on page 530

UNSTACK: Session command for separating a column into multiple columns on page 127

UPRIMECHART: Session command for creating a Laney U' chart on page 519

VARTEST: Session command for performing an equal variances test on page 249

VASAMPLING: Session command for creating or comparing variables acceptance plans on page 691

VASPECT: Session subcommand for specifying the aspect ratio of the data box on page 1091

VBOX: Session subcommand for specifying the display of the box that surrounds the data on a graph on page 1092

VFACTORIAL: Session command for analyzing variability in a 2-level factorial design on page 327

VFIELD: Session subcommand for specifying the dimensions of the field of view in object units on page 1092

VMASK: Session command for creating a two-sided CUSUM chart on page 556

VORDER: Session command for controlling the order for text categories to be processed by Minitab commands

on page 137

VPOSITION: Session subcommand for specifying the view position as a ratio on page 1092

VPREPROCESS: Session command to preprocess responses for analyze variability on page 326

Minitab Statistical Software Using Session Commands

VUP: Session subcommand for specifying which direction is up in relation to the data box on page 1092

WALSH: Session command for calculating pairwise averages on page 912

WDIFF: Session command for calculating pairwise differences on page 912

WHILE and ENDWHILE: Session commands for repeating a block of commands depending on a logical expression

on page 1116

WHISKER: Session subcommand for controlling the display of whiskers on a boxplot on page 1093

WINTERVAL: Session command for calculating a Wilcoxon confidence interval on page 910

WOPEN: Session command for opening a worksheet on page 45

WORKSHEET: Session command for making a worksheet active, for closing a worksheet, or for renaming a worksheet

on page 48

WPREDICTIONS: Session command for performing warranty predictions on page 755

WRITE: Session command for writing data to the screen or a data file on page 49

WSAVE: Session command for saving a worksheet file on page 51

WSLOPE: Session command for calculating pairwise slopes on page 912

WSTACK: Session command for stacking worksheets on page 128

WTEST: Session command for performing a 1-sample Wilcoxon test on page 909

WTITLE: Session subcommand for specifying the title of the output pane on page 1093

XBARCHART: Session command for creating an Xbar chart on page 454

XDACTIVATE: Session command for activating a link on page 57

XDADD: Session command for adding a new link on page 57

XDDEACTIVATE: Session command for deactivating a client link on page 59

XDEXEC: Session command for executing a command in a remote application on page 60

XDGET: Session command for performing a one-time data transfer on page 60

XDREMOVE: Session command for deleting an established link on page 61

XPPOINT: Session command for sending output to Microsoft PowerPoint on page 53

XRCHART: Session command for creating an Xbar-R chart on page 433

XSCHART: Session command for creating an Xbar-S chart on page 439

XTABS: Session command for displaying one-way, two-way, and multi-way tables for categorical variables on page

905

XWORD: Session command for sending output to Microsoft Word on page 53

ZMRCHART: Session command for creating a Z-MR chart on page 485

Minitab Statistical Software Using Session Commands

ZONE: Session command for creating a zone chart on page 473

ZONE: Session subcommand for power and sample size for a 1-sample Z-test on page 940

What are session commands?

Session commands are a command language that you use, instead of the menus and interface, to access most functions

in Minitab. Session commands are especially useful in macros.

You can type session commands in the Command Line pane.

Session command syntax notation

Generally, the syntax of a command includes the command name, and then one or more arguments upon which the

command operates.

Usually a command operates on one or more arguments (also called parameters) that you specify; they can be columns,

constants, matrices, numbers, file names, or text strings. For example, "C1" is the argument in the following command,

which tells Minitab to draw a histogram of the data in column C1:

HISTOGRAM C1

Minitab Help uses the following typographical conventions for describing the syntax of individual commands.

Denotes a constant such as 8.3 or K14.

Denotes a column such as C13 or 'Height'.

C...C

Denotes a list of one or more columns separated by spaces.

Denotes a matrix such as M5.

Denotes either a constant or column, and sometimes a matrix.

[ ]

Denotes an optional argument, for example [K1].

Symbols to use with session commands

Use the following symbols with any session commands or subcommands.

Comment symbol (#)

Place the comment symbol # anywhere on the line to tell Minitab to ignore the rest of the line. For example:

DESCRIBE C1 #This is a comment

Minitab Statistical Software Using Session Commands

Missing value symbol (*)

Place the missing values symbol * anywhere a number would normally interact, to represent values that could be

missing. The asterisk should be enclosed in single quotation marks ('*' ). You could use the following command

to copy data from one column to another, omitting rows that have missing values:

COPY C1 C2;

OMIT C1 = '*'.

Using a subcommand

Many session commands have subcommands. To use a subcommand, complete the following steps.

Note When you use a command with no subcommands, you do not have to type any punctuation mark after the command line.

1. Type the main command and end the main command line with a semicolon (;).

2. Press Enter to move to the next line.

3. Type as many subcommands as you need, ending each with a semicolon (;) and pressing Enter after each.

4. End the last subcommand with a period (.).

If you forget to end the last subcommand with a period, you can type the period all by itself on the next line.

Using session commands in the Command Line

pane and the History pane

Sometimes, it is convenient to copy a previously executed session command (or sequence of session commands) from

the Command Line pane or the History pane, make minor changes if necessary, then execute the changed command(s).

Use the Command Line pane to type, edit, and enter commands.

The Command Line pane and the History pane can be viewed together, docked on the right side of the application

frame. If the Command Line pane and the History pane are not visible, then do one of the following:

•

Choose View > Command Line/History.

•

Use Ctrl+K shortcut to open the Command Line/History panes.

Note If the panes are already in view, Ctrl+K puts focus into the Command Line pane, but will not toggle the pane closed.

Executing session commands in the Command Line pane

There are several ways to enter command language into the Command Line pane. For instance, you can:

•

Type the commands and subcommands directly into the pane. Use the Enter key to go to the next line. For

information on arguments, go to Session command syntax notation on page 27 or Using a subcommand on page

28.

•

Highlight the commands in the History pane and either copy and paste to the Command Line pane, or click Copy

to Command Line.

•

Paste text from other applications.

Press Run to execute the command language.

Minitab Statistical Software Using Session Commands

Using the History pane

The History pane provides a convenient list of the commands that you have used in your project.

You can select commands and subcommands as list items and can multi select both contiguous and non-contiguous

items in the list. Selected items can be copied to clipboard or dragged to the Command Line pane. When pasted into

the Command Line pane, you can edit the command language before running it.

You can also create a Minitab macro file for a routine analysis by first working through the steps using the menus,

highlighting portions of session commands, and saving them as a macro or text file type.

Print the contents of the History pane

You can print the contents of the History pane. Right-click the selected text, then choose Print History.

Save the contents of the History pane

You can save the contents of the History pane in a text file. Right-click the selected text, then choose Save History

As. Choose your file name and file type.

Rules for entering session command arguments

Arguments specify data characteristics, such as location or titles. They can be variables (columns, constants, matrices)

as well as text strings or numbers. For information on argument notation, go to Session command syntax notation on

page 27.

Variables

•

Enclose variable names in single quotation marks (for example, HISTOGRAM 'Salary'). Certain commands, such as

ANOVA, GLM, and the high-resolution graphics commands do not require quotation marks, but all commands

work properly when quotes are used.

•

In arguments, variable names and variable numbers can be used interchangeably. For example, the two following

commands do the same thing (if C1 is named 'Sales'):

DESCRIBE C1 C2

DESCRIBE 'Sales' C2

•

You can abbreviate a consecutive range of columns, stored constants, or matrices with a dash. For example, PRINT

C2-C5 is equivalent to PRINT C2 C3 C4 C5.

•

You can use a stored constant (such as K20) in place of any constant. You can even use stored constants to form

a range such as K20:15, which represents all integers from the value of K20 to 15.

Text strings

Enclose text strings, such as labels or file names, in double quotes (for example, TITLE "This is My Title"). In earlier

versions of Minitab, text was enclosed in single quotes. Although this still works, it is no longer recommended, and

can cause a conflict with column and constant names.

Numbers

•

Do not enclose numbers in quotes unless you want the numbers to appear as text.

Minitab Statistical Software Using Session Commands

•

To specify a range of numbers, abbreviate the sequence using these conventions:

1:4 expands to 1 2 3 4

4:1 expands to 4 3 2 1

1:3/.5 expands to 1.0 1.5 2.0 2.5 3.0

The session command SET on page 80 includes additional abbreviation conventions.

Rules for entering session commands

A session command consists of one main command, and may have one or more subcommands. Arguments and

symbols may also be included in the command.

•

Subcommands, which further define how the main command should be carried out, are optional unless otherwise

specified.

•

Arguments, which specify data characteristics, may be included one or more times for both the main command

and subcommands.

•

Symbols, which assist in controlling the session language, can also be included in session commands.

•

Commands and column names are not case-sensitive. You can type them in lowercase, uppercase, or any combination.

•

You can abbreviate any session command or subcommand by using the first four letters.

•

Enter only one command or subcommand per line.

•

Close all but the last line of a command with semicolons. Close the last line with a period.

•

Some subcommands have their own subcommands. The order in which you give these subcommands determines

what subcommand or command they modify. You can use many subcommands more than once in a command.

Note Some commands, called %macros, are macros that are invoked by typing % followed by the full macro name (you cannot abbreviate

macro names).

Interrupting session command execution

To interrupt the display of data from a command or the execution of a macro, type Ctrl+Break. In a macro, Minitab

finishes executing the current command, then exits the macro. Display of data is halted as soon as possible.

Updates for release 19.1

The following section includes the session commands that are new, changed or obsolete for Minitab 19.

Input and history

In Minitab 19.1, choose View > Command Line/History to open the Command Line pane and the History pane. In

the Command Line pane, enter session commands and run macros. Use the History pane to see session commands

that ran and to copy those session commands.

Minitab Statistical Software Using Session Commands

Obsolete continuation character (&)

In previous versions the & symbol indicated that a command continued on the next line, for example:

PLS C18 = C1-C17 c1*c2 c1*c3 c1*c4 c1*c5 c1*c6 c1*c7 c1*c8 c1*c9 c1*c10 c1*c11&

c1*c12 c1*c13 c1*c14 c1*c15 c1*c16 c1*c17;

In Minitab 19.1, session commands with an & symbol create errors. Instead, type everything on 1 line.

PLS C18 = C1-C17 c1*c2 c1*c3 c1*c4 c1*c5 c1*c6 c1*c7 c1*c8 c1*c9 c1*c10 c1*c11 c1*c12

c1*c13 c1*c14 c1*c15 c1*c16 c1*c17;

Adding comments and notes

In Minitab 19.1, each instance of the NOTE command creates a new output tab. To keep different notes together,

surround all of the notes and output that you want on one output tab with MTITLE and ENDMTITLE.

New commands

Resampling commands

BTFT

Bootstrapping for 1-sample function

BTPR

Bootstrapping for 1 proportion

BTTM

Bootstrapping for 2-sample means

RNMN

Randomization test for 1-sample mean

RNPR

Randomization test for 1-sample proportion

RNTM

Randomization test for 2-sample means

Design of experiments

BFFA

Analyze a binary response variable for a 2-level factorial design.

BGFA

Analyze a binary response variable for a general full factorial design.

BRSREG

Analyze a binary response variable for a response surface design.

BSCREEN

Analyze a binary response variable for a screening design.

Minitab Statistical Software Using Session Commands

MDESIGN

Modify a design that is in the worksheet.

VPREPROCESS

Pre-process responses for analyzing the variability of repeat or replicate measurements for a factorial design.

Obsolete commands

CONSTANT/NOCONSTANT

Use subcommands to for an individual analysis to control the estimation of the constant term.

DIR

List the names of files in a directory.

GPAUSE

Specify the number of graphs to display before you are prompted to save or discard open graphs. In Minitab

19.1, the number of graphs does not have a fixed limit.

GPRINT

Print a graph window. In Minitab 19.1, all output is in tabs instead of windows.

GVIEW

Open a .MGF format image. Minitab does not save images in this format anymore.

INSERT

Insert rows of data into the worksheet. Consider WOPEN and READ.

Set the maximum width for input to the session window. Minitab 19.1 does not limit the input width in the

Command pane.

JOURNAL/NOJOURNAL

Save session window lines to a text file. In Minitab 19.1, save the History pane.

MRESET

Restore environment settings to pre-macro conditions. In Minitab 19.1, restoration occurs at the end of every

macro.

Set the width of output in the session window. In Minitab 19.1, the output pane does not have a fixed width by

number of characters.

PLUG/NOPLUG

Respond to errors from the macro processor. In Minitab 19.1, the macro processor stops when it encounters an

error.

TITLE/NOTITLE

Display a title above session window output. In Minitab 19.1, use MTITLE/ENDMTITLE to add a title for an output

tab and to group output on a single output tab.

Minitab Statistical Software Using Session Commands

Changes to subcommands

Opening and saving files

READ

The FORMAT subcommand does not use the T format.

RETRIEVE

The PORTABLE subcommand is obsolete. MTP format files are not compatible with Minitab 19.1.

The REPLACE/NOREPLACE subcommands are obsolete. If you save a file with the same filename, Minitab overwrites

the file.

The GRAPH does not support the parameter MGF. Minitab no longer saves images in this format.

The PROJECT subcommand saves the project in the MPX format. The subsubcommand PASSWORD specifies a

password that protects the file.

SAVE

The RELEASE subcommand accepts only 19 as a value. The tabbed output and other changes cannot be saved

to an earlier version of Minitab.

The REPLACE/NOREPLACE subcommands are obsolete. If you save a file with the same filename, Minitab overwrites

the file.

The GRAPH does not support the parameter MGF. Minitab does not save images in this format anymore.

The PROJECT subcommand saves the project in the MPX format. The subsubcommand PASSWORD specifies a

password that protects the file.

Analysis of linear models

NLOG

OLOG

The TOLERANCE subcommand uses 1 parameter. Minitab uses the same value for all tolerances.

REGRESSION

The GPARETO subcommand makes a Pareto chart of the effects.

Changes to correlation

CORRELATION

The NOPVALUES subcommand is obsolete. P-values are in the new pairwise correlation table.

The following subcommands control the amount of output:

•

TMETHOD

•

TCORRELATION

•

TPCORRELATION

•

NODEFAULT

•

GMPLOT

The following subcommands are for storage:

•

SCORRELATION

Minitab Statistical Software Using Session Commands

•

SCIS

The CONFIDENCE subcommand sets the confidence level for confidence intervals.

New subcommands

Creation of designed experiments

BBDESIGN

CCDESIGN

EVDESIGN

FDESIGN

FFDESIGN

MIXREG

OADESIGN

OPTDES

PBDESIGN

SCDESIGN

SLDESIGN

SPDESIGN

The DESIGN subcommand stores the design columns in the worksheet. The new subcommand replaces

subcommands that required individual specification.

OPTDES

The COPY subcommand moves columns that are not part of the design, such as COVARIATES, to the new worksheet

where you store rows for an optimal design.

ROBUST

The DOE subcommand stores the model information in a Minitab design object.

Minitab Statistical Software Using Session Commands

Analysis of linear models

FFAC

GFAC

GLM

GZLM

REGRESS

RSREGRESS

SCREEN

VFAC

The FINFORMATION subcommand specifies forward stepwise selection with information criterion.

Capability analysis

BWCAPA

CAPA

MCAPA

The UCPM and LCPM subcommands store one-sided confidence limits of the Cpm statistic.

The ONECI sub-subcommand to the CONFIDENCE subcommand specifies one-sided confidence intervals for the

capability metrics.

The PPM subcommand replaces the percentage calculations with parts per million (PPM).

Changes to subcommands

FORMAT

The FORMAT subcommand, for commands such as READ, does not use the T format.

GSAVE

•

REPLACE is obsolete.

•

NOREPLACE is obsolete.

RELEASE

The RELEASE subcommand to SAVE accepts values of 19 or higher.

WOPEN

Changes to the subsubcommands of the subcommand FTYPE:

•

XLSX is new.

•

REPLACE is obsolete.

•

NOREPLACE is obsolete.

Minitab Statistical Software Using Session Commands

WSAVE

Changes to the subsubcommands of the subcommand FTPYE:

•

MINITAB accepts values of 19 or higher.

•

XLSX is new.

•

U8TEXT is new.

•

U8CSV is new.

•

REPLACE is obsolete.

•

NOREPLACE is obsolete.

•

WEBPAGE is obsolete.

Obsolete subcommands

FNUMERIC

The subsubcommand CULTURE to the subcommand CURRENCY is obsolete.

READ

The subcommand TAB is obsolete. Use WOPEN instead of READ to open tab-delimited data.

WSAVE

REPLACE and NOREPLACE are obsolete. If you save with the same filename, Minitab overwrites the file.

Commands that are unavailable in the web app

The following commands are not currently supported in exec or macro files in the Minitab web app:

DescriptionCommand

Display current directory or change current directory to another pathCD

Run a MTB fileEXEC

In macros, transfer control back to interactive Minitab; in execs, exit MinitabEXIT

Save a graph fileGSAVE

Invoke commands specified on customized My MenuMYME

Create new projectNEW

Stop writing output to an open/active text fileNOOU

Import data from a database fileODBC

Save output to a text fileOUTF

Set character width of outputOW

Shift control to keyboard for interactive user inputPAUS

Change profilePROF

Python script commandPYSC

Exit MinitabQUIT

Minitab Statistical Software Using Session Commands

DescriptionCommand

Import data from a text file into a worksheet (READ is allowed without FILE)READ; FILE

Restart MinitabREST

Resume macro execution from interactive keyboard entryRESU

Retrieve a project file in local macrosRETR

Import data from a text file (TSET is allowed without FILE)TSET; FILE

Save a project file in local macrosSAVE

Import data from an ASCII file (SET is allowed without FILE)SET; FILE

Exit MinitabSTOP

Open worksheetWOPE

Save worksheetWSAV

Writes data in the specified columns or constants to a data file (WRITE is allowed without FILE)WRITE; FILE

Send output to Microsoft

PowerPointXPPO

Send output to Microsoft

WordXWOR

Minitab Statistical Software Using Session Commands

Opening, Saving, and Printing Files

END: Session command for ending data input

END

Ends data input.

Type END following the last data line typed after READ (for information, go to READ data into a matrix on page

43 or READ data into columns on page 41), or SET on page 80. This ensures that any diagnostic messages

concerning data lines will be printed before the next operation is carried out.

GSAVE: Session subcommand for saving a graph in

a file

GSAVE "file_name"

GSAVE K

Saves the graph in a file.

The default file name is Minitab.PNG. You can specify a custom file name in double quotation marks ("file_name"),

or as a stored text constant (K). You can also use any of the following subcommands to save the graph in a different

graphics format.

Some graph commands—for example, HISTOGRAM C1 C2 C3—generate more than one graph. If you include

the GSAVE subcommand with such a command, Minitab saves multiple files. Minitab gives each file a different

file name. Minitab uses the first five characters of the name you specify, then appends a number (001, 002, and

so on), for up to 300 files.

JPEG

JPEG color

PNGB

PNG grayscale

PNGC

PNG color

TIFB

TIF grayscale

TIF

TIF color

BMPB

BMP grayscale

Minitab Statistical Software Opening, Saving, and Printing Files

BMPC

BMP color

GIF

EMF

RESOLUTION K

Saves the graph at a resolution of K dots per inch.

ODBC: Session command for importing data from

a database file

ODBC

Imports data from a database file, such as one saved by Microsoft Access, Oracle, dBASE, Sybase, or SAS, into the

Minitab worksheet.

With ODBC (open database connectivity), you can import a subset of data, such as data collected during a certain

month, into the Minitab worksheet. ODBC adds data to the worksheet to the right of existing columns.

To use the ODBC session commands (for example, in a macro) use this method to identify the correct syntax:

1. Choose File > Query Database (ODBC) to query data.

2. When you have successfully retrieved the data you want, copy the corresponding command language from

the History pane. (Open the History pane by pressing Ctrl+K.)

3. The COLUMNS subcommand is not created when you use the ODBC dialog boxes. If you are creating a local

macro, add this subcommand yourself. Remember that in a local macro, columns must be declared before

they are used as arguments in any commands. The ODBC session command can also query more than one

table at a time.

CONNECT "connection string"

CONNECT specifies complete information pointing to the data source you want to query on your network.

The text string argument can be very long.

SQLSTRING "sqlstring"

SQL specifies the query that selects a subset of the data. The text string can be up to 16384 characters in

length.

COLUMNS k...k

COLUMNS specifies which columns of the Minitab worksheet should hold the data. k can be an integer from

1 to 4000.

Minitab Statistical Software Opening, Saving, and Printing Files

The COLUMNS subcommand is required in local macros. In global macros or Execs, or when using Minitab

interactively, executing the ODBC command without the COLUMNS subcommand places new data at the

end of the global worksheet.

Note You can specify a range of columns with a colon, for example: 1:5. Within Minitab or in a global macro you can also

use stored constants that contain integers, for example 1:k1 where k1=5.

OUTFILE and NOOUTFILE: Session commands for

saving a Minitab session in a text file

OUTFILE "filename"

OUTFILE K

OUTFILE saves your Minitab output in a text file. You can specify the filename as either the name of the file in

double quotation marks, or as a stored text constant.

After OUTFILE is typed, Minitab sends a copy of the commands and output that you see in the History pane to

a text file. OUTFILE is in effect until you type NOOUTFILE or you exit Minitab. If you type OUTFILE again, with the

same file name, output is appended to the end of this file.

The file is a standard text file, which can be printed and edited by any editor or word processor. Unless you specify

a different file extension, Minitab adds the extension LIS to the file name.

You can use OUTFILE to get a printout of your worksheet. Suppose your current worksheet contains data in the

first 10 columns. For example, the following commands save a copy of your worksheet is to a file named Sales.LIS

in your default directory.

OUTFILE "Sales"

PRINT C1-C10

NOOUTFILE

Closing the outfile

NOOUTFILE

NOOUTFILE closes an open outfile. If you type OUTFILE again with the same file name, input lines are appended

to the same file.

PRINT: Session command for displaying columns,

constants, or matrices in the output pane

PRINT E...E

Displays data in the output pane. You can display one or more (or any mixture of) columns, stored constants, or

matrices.

Minitab Statistical Software Opening, Saving, and Printing Files

If you mix columns, constants, and matrices in one PRINT command, they are listed in the following order: first

all matrices and constants (in the order you specified), then all columns (in the order you specified). For example,

the following command prints M1, K2, K1, and M2, then C1, C4, and C3.

PRINT C1 C4 M1 K2 C3 K1 M2

If a number is too large to fit in the space allowed (and FORMAT is not used), the number is displayed in exponential

format.

FORMAT (format statement)

The FORMAT subcommand specifies where and how to print data on the output line. If you PRINT columns

using FORMAT, only the data in columns is displayed, not column names. For more information, go to Valid

format items on page 1186.

Minitab chooses the output format (i.e., number of decimal digits printed). If you want to control this yourself,

use the FORMAT subcommand, or you can right-click on the output and select Decimal Places.

The following command prints numbers single-spaced, five numbers on each line, each number in a field of

ten spaces.

PRINT C11-C15;

FORMAT (5F10).

READ data into columns

READ C...C

Reads in data, row by row, that you type from the keyboard, or that you import from a text file. You cannot type

comments on a data line when the FORMAT subcommand is used.

READ enters new data into columns, replacing any data already in those columns, if it exists. For information on

entering data into a matrix, go to READ data into a matrix on page 43.

When you enter data manually, type END. after you enter your final value.

When you use READ, you can use a space or a comma to separate data entries. For example:

READ C1 C5.

1 2

3,4

END.

For details on using this command without subcommands to select data entry options, go to Using READ without

subcommands on page 1183.

FILE "filename"

Inserts data from the specified text file. You may specify the filename as either the name of the file in double

quotes, or a stored text constant. If the file has an extension other than DAT and/or if it is not in your current

directory, include the file extension and the path within the single quotation marks. For example, use the

following command to read a copy of the file SALES.ASC stored in the subdirectory JANUARY underneath

the directory SMITH on the C drive.

READ C1-C5;

FILE "C:\SMITH\JANUARY\SALES.ASC".

FORMAT (format statement)

Include a format statement, within parentheses to specify precisely how to enter data into the worksheet.

The entire expression within the parentheses is repeated once for each record. For more information, go to

Using READ with FORMAT on page 1183.

Minitab Statistical Software Opening, Saving, and Printing Files

The FORMAT subcommand is useful when you want to skip over spaces, read data that have no spaces

between them, insert decimal points in numbers, or read in text data, date/time data, or currency data.

Format items may be combined together. For example, the following command reads the name in the first

20 spaces of each data line into Name (C12), skips the next 10 spaces (spaces 21 through 30), then reads the

number in space 31 into C1, the number in space 32 into C2, ..., the number in space 40 into C10.

NAME C12 'Name'

READ 'Name' C1-C10;

FILE "MYDATA";

FORMAT(A20, 10X, 10F1).

Minitab has a special date/time (DT) format which works as shown below. This says to read the date/time

value in the first 8 spaces in the file into C1, and that the format of the date/time data in the file is m/d/yy.

READ C1;

FILE "DATEDATA";

FORMAT(DT8m/d/yy).

The following example shows the use of a decimal indicator, repeat factor in front of parentheses, and the

slash.

READ C11-C15;

FILE "EMPLOYEEDATA";

FORMAT (F2.1, 2(1X,F3), F4/F2).

This example uses two data lines for every row read. From the first line, the value of C11 is in spaces 1 and

2. The first value is a whole number and the second value is in the tenths place. The format skips space 3.

Then, C12 is read from spaces 4 to 6. The format skips space 7. Then, C13 is read from spaces 8 to 10, which

repeats the pattern inside of the parentheses. C14 is read from spaces 11 to 14. In response to the /, reading

moves to the second data line, and C15 is read from spaces 1 and 2. For more information, go to Valid format

items on page 1186.

NOBS K

The NOBS subcommand specifies the number of observations (rows) to be inserted. If an END on page 38

subcommand or end-of-file is encountered before K observations are inserted, NOBS is ignored. NOBS is

useful when you want to insert just the first portion of a file. It is also useful for Prompting a user for

information on page 1177.

SKIP K

Tells Minitab to skip K lines at the top of the data file before beginning to add data into the file. This is most

useful when you have one or more lines of text, such as column names and titles, at the top of a data file

that you want to import into Minitab.

With READ; FILE only

DECIMAL works with READ when only when reading a file. DECIMAL does not work with READ if you type data.

DECIMAL ","

DECIMAL "."

Specifies a comma or period as a decimal separator.

Minitab Statistical Software Opening, Saving, and Printing Files

READ data into a matrix

READ K K M

Puts numbers into a matrix. To input data to columns, go to READ data into columns on page 41. You can specify

the filename as either the name of the file in double quotes, or a stored text constant. If the file has an extension

other than DAT and/or if it is not in your current directory, include the file name extension and the path within

quotation marks.

You can use either spaces or commas to separate the data in the matrix.

You must specify the dimension of the matrix in the READ command. The first K gives the number of rows, the

second K the number of columns. The M is the matrix identifier for storage. If a file name is not used, READ is

followed by data lines, each containing one row of the matrix. The following command creates the following

matrix.

Command

READ 3 4 M2

1 2 3 4

5 6 7 8

9 10 11 12

END

Matrix

1 2 3 4

5 6 7 8

9 10 11 12

FILE "filename"

FILE K

Reads or inserts data from the specified text file.

RESTART: Session command for restarting

RESTART

Note You cannot use RESTART in a global macro. For more information, go to Session commands that are not allowed in macros

on page 1179.

Allows you to start over again.

RESTART erases the worksheet, cancels any controls in effect. RESTART also closes all open files such as OUTFILE.

Minitab Statistical Software Opening, Saving, and Printing Files

RETRIEVE: Session command for retrieving a saved

worksheet or project

RETRIEVE "filename"

RETRIEVE K

Note The menu command File > Open and the session command WOPEN on page 45 also open Minitab saved worksheets and

Excel files (and many other types of files). They provide several useful options that are not available with RETRIEVE.

Use the main command by itself to retrieve a saved worksheet and add the file to the current project. With

subcommands you can open a project or add one or more worksheets from a project to the current project. You

can specify the filename as either the name of the file in double quotes or as a stored text constant.

If you omit the file name and the current folder contains a file named Minitab.MWX or Minitab.MTW, then Minitab

opens that file.

Note You cannot use RETRIEVE in a local macro. For more information, go to Session commands that are not allowed in macros on

page 1179.

PROJECT

Note You cannot use PROJECT in a global macro. For more information, go to Session commands that are not allowed in

macros on page 1179.

Specifies that the file after RETRIEVE is a Minitab project file (MPX, MPJ). If you do not want the prompt,

use the SAVE command with the PROJECT subcommand before you use the RETRIEVE command.

PASS "password"

To retrieve a password-protected file, specify the password.

SAVE: Session command for saving a worksheet or

project

SAVE [K]

Note You cannot use SAVE in a local macro. For more information, go to Session commands that are not allowed in macros on page

1179.

Saves a worksheet or project. You can specify the filename as either the name of the file in double quotation

marks, or a stored text constant. When no subcommands are specified, worksheet (MWX) is the default file type

for SAVE.

A saved worksheet file contains all data, stored constants, matrices, column names, and missing value information.

Minitab automatically replaces an existing file if you save with the same filename.

You can open saved worksheets with RETRIEVE on page 44 or WOPEN on page 45.

PROJECT

Specifies to save as a Minitab Project file (MPX).

Minitab Statistical Software Opening, Saving, and Printing Files

WSONLY

Use the subcommand WSONLY to save only worksheets with the saved project.

PASSWORD "password"

Specify a password to use to open the file. Enter the password within double quotation marks. To remove

password protection, use the argument "".

RELEASE K

Specifies the earliest version of Minitab that can open the file. For Minitab 19 and higher, 19 is the earliest

valid argument. You cannot specify a version newer than the version of Minitab that you have.

STOP: Session command for closing Minitab

STOP

Closes Minitab.

WOPEN: Session command for opening a worksheet

WOPEN K

Note You cannot use WOPEN in a local macro. For more information, go to Session commands that are not allowed in macros on

page 1179.

Opens a worksheet. For example, to open a data set named PULSE, enter the following command.

WOPEN "PULSE"

When you open a file, you copy the contents of the file into the current Minitab project. Any changes that you

make to the worksheet while in the project do not affect the original file.

FTYPE

Specifies the type of the file to open. Use one of the following subcommands for FTYPE:

MINITAB

XLSX (For Excel files with a .xlsx extension)

EXCEL (For Excel files with a .xls extension)

XMLEXCEL

TEXT

CSV

You must specify the file type, such as EXCEL, to use some of the other subcommands, such as VNAMES. If

you do not specify the file type, then Minitab considers the file extension. For example, if you enter WOPEN

"Mywork.xls" but do not specify a file type, then Minitab opens the file as an Excel file. However, if that

Excel file were saved with the name Mywork.abc, Minitab would not recognize the file as an Excel file, and

would display an error message or open the file incorrectly.

When you open a Minitab worksheet (with FTYPE; MINITAB), you do not need to specify a release number.

Minitab Statistical Software Opening, Saving, and Printing Files

FIELD

FIELD allows you to specify how fields are delimited.

Use one of the following subcommands to specify what character denotes the sections of data to be placed

into each field. If FIELD is not specified, Minitab uses the default the default delimiter for the file type.

TAB

COMMA

SEMICOLON

SPACE

PERIOD

CUSTOM "K"

TDELIMITER

Specifies how text fields are delimited.

DOUBLEQUOTE

Use when the columns of data are separated by a double quotation mark.

SINGLEQUOTE

Use when the columns of data are separated by a single quotation mark.

NONE

Use to have text that is separated by a blank entered into its own column.

CUSTOM K

Use if the columns are separated by a character other than those listed above and enter the character

as K.

DECSEP

Specifies how decimals are separated.

COMMA

Specifies that decimals are separated by a comma.

PERIOD

Specifies that decimals are separated by a period.

MISSING

Specifies how to write out a missing value.

TEXT K K

Denotes text columns, with the first K as the "missing" marker in the file's original form, and the second

K as the one or more words, characters, or spaces that you want Minitab to replace these markers with.

The default Minitab marker for missing text is a space.

NUMERIC 'K' K

Denotes numeric columns, with the first K as the missing marker in the file's original form, and the

second K as the digits, characters, or spaces that you want Minitab to replace these markers with. The

default Minitab marker for missing numeric data is an asterisk (*). The missing value symbol (first K)

must be enclosed in single quotation marks.

Minitab Statistical Software Opening, Saving, and Printing Files

DATA

Specifies where to begin reading data in the file to be opened.

IGNOREBLANKROWS

Skips over blank data rows. If you don't use this command, Minitab reads blank data rows as missing

values.

EQUALCOLUMNS

Adds missing values (*) to shorter columns to so that all columns have the same number of rows.

CLEAN

Removes nonprintable characters and extra spaces from text columns.

CASE

Corrects case mismatches in text columns by applying the capitalization of the first occurrence of a text

value to all matching values in the column.

SHEET K

Controls the way the K

worksheet in the file is read. SHEET can be used one time for each worksheet in the

file.

EXCLUDE

Stops the worksheet from being imported.

VNAMES K

Specifies the row number K that contains the variable names. −1 indicates no variable names. 0 indicates

that the first row that is being imported is the variable names row.

FIRST K

Specifies the row number K to begin reading the data. If you do not choose a variable names row,

Minitab begins reading data from the first available row in the file.

NROWS K

Specifies the number of data rows. Enter a number to read a portion of the file. For example, if you enter

50, Minitab reads only the first 50 rows beginning with the first row of data. By default, Minitab reads

all rows from the file into the current worksheet.

VARIABLE K

Controls the way the K

variable in the worksheet is read. It can be used once for each variable in the

worksheet.

EXCLUDE

Do not import this variable.

NAME K

Assigns a name to the variable. Names can be up to 31 characters long, and can include any letters

and numbers except the symbol ' (single quotation mark) or # (pound sign). Names cannot begin

or end with a blank or consist entirely of the symbol * (asterisk). You also can name your variables

directly after the worksheet is open.

TEXT

Specifies that the data type is text.

NUMERIC

Specifies that the data type is numeric.

Minitab Statistical Software Opening, Saving, and Printing Files

DATETIME

Specifies that the data type is date/time.

COLUMN C

Specifies which column of the worksheet (C) the variable is placed in.

WORKSHEET: Session command for making a

worksheet active, for closing a worksheet, or for

renaming a worksheet

WORKSHEET K

You cannot use WORKSHEET in a local macro. For more information, go to Session commands that are not allowed

in macros on page 1179.

Specifies K as the active worksheet. If no worksheet is specified, then session commands work on the active

worksheet.

When you are working in Minitab, any session command you use works on the active worksheet. The active

worksheet is the worksheet associated with the active Data tab.

You can also click a worksheet to activate it. If no worksheet is active, the session command acts on the worksheet

that was most recently active.

Note A worksheet can contain up to 4000 columns, 1000 constants, and up to 10,000,000 rows depending on how much memory

your computer has.

If you run subcommands, only the last subcommand works. To rename and close a worksheet, you must use more

than one instance of the main session command.

Closes the worksheet.

NOPROMPT

Specifies not to prompt to save when saving the worksheet.

CURRENT

Specifies to make the named worksheet active.

RENAME K

RENAME "text"

Renames the worksheet.

Minitab Statistical Software Opening, Saving, and Printing Files

WRITE: Session command for writing data to the

screen or a data file

WRITE E...E

Writes data in the specified columns or constants to the screen or to a data file (also called text or ASCII file). You

can specify the filename as either the name of the file in double quotes, or a stored text constant.

WRITE exports your data to a data file which you can import into other applications, print on your printer, or enter

into Minitab with READ (for more information, go to READ data into columns on page 41 or READ data into a

matrix on page 43) or SET on page 80. WRITE prints the columns vertically, very close together, with no column

names or row numbers and prints constants horizontally, whether they are stored numeric or text constants.

If you omit the file name, WRITE displays the columns or constants on your screen. If you specify a file name but

omit the extension, WRITE adds the default extension DAT.

Use the FORMAT subcommand to write data with a fixed format, for example, columns not separated by spaces.

By default, if you WRITE to a file that already exists, Minitab asks you whether or not you want to replace the file

before proceeding. You can use the subcommands REPLACE and NOREPLACE to override Minitab's default

behavior.

WRITE normally creates a data file. It can also be used to print columns or constants on your screen or on paper.

The output is very compact. With columns, there is no header giving the column name, and there are no row

numbers on the left. Columns are always output vertically. Constants are output horizontally, whether they are

stored numeric or text constants.

A data file created with the WRITE command can be transferred to different computer types and read by other

programs. On most computers, the default file name extension is DAT.

•

The format of the output is adjusted to make the data as compact as possible. Thus, the number of columns

that can be put on one line varies with the data.

•

If a format is not specified, and if very wide text items are to be printed (e.g., 80 wide), and if the output width

is not wide enough to print the entire item, the items will be truncated on the right.

•

When writing columns of unequal length to a file, Minitab makes them equal by adding missing value symbols

(*) to the short columns.

Note The menu command File > Save Worksheet As and the session command WSAVE on page 51 can also save the current

Minitab worksheet as a text file, and many other file types as well. It also provides several useful options not available with WRITE.

FILE "filename"

Specify to write to a data file (also called text or ASCII file). You may specify the filename as either the name

of the file in double quotes, or a stored text constant. If you specify a file name but omit the extension, WRITE

adds the default extension DAT. Use the subcommands UTEXT and TEXT to specify Unicode or ANSI text for

the file.

UTEXT

Saves the file in Unicode format.

TEXT

Saves the file in text format.

FORMAT

A format specification, similar to a Fortran format, may be given on this subcommand. For more information,

go to Valid format items on page 1186.

Minitab Statistical Software Opening, Saving, and Printing Files

The format specifies where the data will appear on the output lines, and the number of decimal places printed.

FORMAT works the same way with WRITE as it does with READ (with the exception of the currency format,

which works with READ but not WRITE). For more information, go to READ data into columns on page 41

or READ data into a matrix on page 43.

The following table shows how 123.456 is output with different formats.

OutputFormat

123.456F7.3

123.46F7.2

123I7

1.23456E+02E11.6

123.456G7

If you create a file with WRITE, using a FORMAT, then the READ command (for more information, go to READ

data into columns on page 41 or READ data into a matrix on page 43) can read the file with the same format.

To create a file that READ can read without a format (or other programs can read without a format), make

sure that there is at least one space between numbers. One way to do this is with the X format item. For

example, the following command creates a file which can be read without a format.

WRITE C1-C5;

FILE "FileA";

FORMAT ( 5(1x, F11) ).

Missing numeric data are output as * in the right-most space of the field. Missing text data are output as

blanks.

You must WRITE columns of equal length.

You can write columns that contain text data with or without using a FORMAT subcommand. You must,

however, use a FORMAT subcommand to READ the file.

Column format

TAB

TAB saves the specified columns, separated by tabs, in a data (also called text or ASCII) file. Do not use FORMAT

with tab-delimited data.

NONAMES

If you do not want to include the column names in the file, use the NONAMES subcommand in addition to

TAB.

File instructions

REPLACE and NOREPLACE allow you to bypass the "REPLACE?" prompt and are most useful in macros.

REPLACE

When you specify REPLACE, Minitab always overwrites the file.

NOREPLACE

When you specify NOREPLACE and you have an existing file with the same name, Minitab generates an error

message and aborts the command or exits the macro.

Minitab Statistical Software Opening, Saving, and Printing Files

WSAVE: Session command for saving a worksheet

file

WSAVE "filename"

Saves all the worksheet data in a file. Use this command to rename your worksheet or save it to a new location.

If your current worksheet is untitled ("Worksheet1" appears in the tab of the worksheet), you can use WSAVE to

specify a name for your saved worksheet file.

FTYPE

Specifies a file format. The sub-subcommands (MINITAB, XLSX, and so on) are the file formats.

You must specify an FTYPE; FORMATNAME combination to use some of the other subcommands, such as

VNAMES.

If you do not use FTYPE when saving a file, Minitab saves the file as a Minitab worksheet for the version that

you have.

MINITAB K

Specifies the Minitab worksheet file type.

When you use the MINITAB file type, specify the earliest version of Minitab that can open the worksheet.

For Minitab 19 and higher, 19 is the earliest valid argument. You cannot specify a version newer than

the version of Minitab that you have.

XLSX

Specifies the Excel file type with a .xlsx extension.

EXCEL

Specifies the Excel file type with a .xls extension.

XMLEXCEL

Specifies the XML Excel file type.

TEXT

Specifies the text file type.

UTEXT

Specifies the unicode text file type.

CSV

Specifies the comma-separated file type.

UCSV

Specifies the unicode comma-separated file type.

NOVNAMES

Saves a file from Minitab into another format without saving the variable names within the file.

DECSP

Specifies the form of decimal separation when you save a text file. The sub-subcommands give the decimal

separator.

Minitab Statistical Software Opening, Saving, and Printing Files

COMMA

Specifies a comma decimal separator.

PERIOD

Specifies a period decimal separator.

FIELD

Specifies how fields are delimited. Choosing one of the options chooses what character denotes the sections

of data to be put into each field. If FIELD is not specified, Minitab uses the default delimiter for the file type.

The sub-subcommands give the delimiter.

TAB

Specifies the tab as the delimiter.

COMMA

Specifies the comma as the delimiter.

SEMICOLON

Specifies the semicolon as the delimiter.

PERIOD

Specifies the period as the delimiter.

SPACE

Specifies the space as the delimiter.

CUSTOM K

Specifies a custom delimiter.

TDELIMITER

Specifies how text fields are delimited.

DOUBLEQUOTE

Use when the columns of data are separated by a double quotation mark.

SINGLEQUOTE

Use when the columns of data are separated by a single quotation mark.

NONE

Use to have text that is separated by a blank entered into its own column.

CUSTOM K

Use if the columns are separated by a character other than those listed above and enter that character

as K.

MISSING

Specifies how to write out a missing value. The sub-subcommands give the values.

TEXT K K

TEXT denotes text columns, with the first K as the "missing" value symbol in the file's original form, and

the second K as the word(s), character(s), or space(s) you want Minitab to replace these symbols with.

The default Minitab symbol for missing text is a space.

Minitab Statistical Software Opening, Saving, and Printing Files

NUMERIC K K

NUMERIC denotes numeric columns, with the first K as the "missing" value symbol in the file's original

form. The default Minitab symbol for missing numeric data is an asterisk (*). The missing value symbol

(first K) must be enclosed in single quotation marks, '*'. The second K can be a number or '*' and is how

the symbol appears in the output file. In Excel files, the missing value symbol becomes an empty cell.

XPPOINT: Session command for sending output to

Microsoft PowerPoint

XPPOINT

Sends Minitab output and graphs to Microsoft PowerPoint so that you can create reports and presentations.

All of Minitab's graphs and output can be sent to a PowerPoint presentation. In PowerPoint, text and titles appear

in the font that is used in Minitab.

Note When you send a graph to PowerPoint, Minitab's Embedded Graph Editor is not available.

APPEND

Exports output into an open Microsoft PowerPoint presentation. The output is starts on a new slide after the

currently active slide.

XWORD: Session command for sending output to

Microsoft Word

XWORD

Sends Minitab output and graphs to Microsoft Word so that you can create reports and presentations.

All of Minitab's graphs and output can be sent to a Word document. In Word, text and titles appear in the font

that is used in Minitab.

Note When you send a graph to Word, Minitab's Embedded Graph Editor is not available.

APPEND

Exports output into an open Microsoft Word document. The output is placed after the cursor location in the

Word document.

Minitab Statistical Software Opening, Saving, and Printing Files

Other Session Commands

PYSC: Session command for running a Python script

PYSC ["filename.py"] ["Args"...]

Runs the Python script that you specify.

The default file extension for Python scripts is .PY. If the file extension is .PY, you do not need to type the file

extension.

The optional argument Args lets you pass arguments to the Python script through sys.argv[1:]. Args can

be any text values separated by a space. Enclose arguments in quotation marks. The default value is None, which

means that the script does not receive any arguments.

For more information on integrating Python and Minitab, go to the Minitab Support Center.

NOSERR

Specifies not to display text from the stderr file in the Output pane in Minitab. For example:

PYSC "test.py";

NOSERR.

SOUT

Specifies to display text from the stdout file in the Output pane in Minitab. For example:

PYSC "test.py";

SOUT.

RSCR: Session command for running a R script

RSCR ["filename.R"] ["Args"...]

Runs the R script that you specify.

The default file extension for R scripts is .R. If the file extension is .R, you do not need to type the file extension.

The optional argument Args lets you pass arguments to the R script through commandArgs(trailingOnly

= TRUE). Args can be any text values separated by a space. Enclose arguments in quotation marks. The default

value is Null, which means that the script does not receive any arguments.

For more information on integrating R and Minitab, go to the Minitab Support Center.

NOSERR

Specifies to not display text from the standard error (message(), warning(), or stop()) console output

in the Output pane in Minitab. The warning console output is where R error messages display when you run

your code in an R integrated development environment. For example:

RSCR "test.R";

NOSERR.

Minitab Statistical Software Other Session Commands

SOUT

Specifies to display text from the standard console output in the Output pane in Minitab. The standard

console output is where the results of commands like print() display in an R integrated development

environment. For example:

RSCR "test.R";

SOUT.

NAME: Session command for assigning names to

columns, stored constants, and matrices

NAME E "name"...E "name"

Assigns names to columns, stored constants, and matrices.

Names must not do the following:

•

Contain more than 31 characters

•

Begin or end with a blank

•

Include the symbols ' or #

•

Start with or consist entirely of the symbol *

•

Be repeated. You cannot use the same name for two variables (columns, stored constants or matrices) in the

same worksheet.

You can refer to a named column, constant, or matrix by either number or name, for example, M1 or "Inverse".

Any time you use a name, enclose the name in double quotation marks. Minitab always prints variable names, if

they exist, on all output involving that variable. You can change the name of a variable by using another NAME

command.

The following example names C1, K1, and K2.

NAME C1"Height" K1"Min" K2"Max"

The following example erases the name of C1.

NAME C1 " "

You can use both upper and lower case letters in a name. When you use the name in a Minitab command, Minitab

considers upper and lower case letters equivalent. When a name is printed in the output, however, the upper and

lower case letters are used. For example, the following commands name the C1 column Height and print the data.

NAME C1"HEIGHT"

PRINT "HEIGHT"

Note You also can type column names into the worksheet as an alternative to the NAME command.

Minitab Statistical Software Other Session Commands

ABORT: Subcommand for exiting a multi-line

command

ABORT

Use ABORT as the next subcommand to exit from a multi-line command without executing it.

HELP: Session command for opening this guide

HELP

Opens the Session Commands guide. The Session Commands guide provides summaries of commands and

specific information on each command and subcommand. You can use session commands as an alternative to

using menu commands, or as a way to build macros for repetitive functions.

Minitab Statistical Software Other Session Commands

Dynamic Data Exchange

XDACTIVATE: Session command for activating a link

XDACTIVATE "application" "topic" "item"

Activates a previously deactivated client link. When you deactivate a link using XDDEACTIVATE on page 59, Minitab

ignores/discards any queued transactions or incoming transactions until you reactivate the link.

You can use XDACTIVATE in execs and global macros, but not in local macros.

You also can pause activity globally.

application

Application is the name of a program that can participate in a DDE transaction. Usually, application is the

name of an EXE file that starts the program, without the .exe extension. For example, in the case of Minitab,

Mtb.exe starts the program, so for the application argument use Mtb.

topic

Topic is a name that depends on the type of application. In applications that use documents or data files,

the topic is often the name of the file. For example, the topic for a Microsoft Word document might be

mywork.doc. The file name can also include a path, as in c:\mywork\mywork.doc.

If one file can contain many documents or subwindows, the Topic can be the name of the file in brackets,

followed by the name of the document or window. For example, Minitab projects can contain many worksheet,

so a typical Topic would be [Minitab] worksheet 1.

Other applications have their own names for topics.

Tip It is a good idea to save files from other applications before establishing any links to or from them. If you establish a link

before saving the file, you may need to change the link name from something like "untitled" to a file name. Likewise, before

linking from Minitab, rename a new worksheet from the default of "Worksheet 1" to something meaningful, such as "First Quarter."

item

Item is a name that depends on the type of application. In Minitab, the item always specifies a row/column

location or rectangular area in the form R4C1:R4C2. You can use all of Minitab column 1 as an item by

specifying C1. A single cell (row 4, column 1) is R4C1. Most spreadsheets use close variants of the R4C1:R3C2

format for items.

XDADD: Session command for adding a new link

XDADD "application" "topic" "item"

Establishes a new DDE link between the specified application program and Minitab. With this command, Minitab

acts as a client, receiving data from the other application.

You often specify XDADD with either the APPEND or REPLACE subcommands to tell where to put the data in the

Minitab worksheet, as shown below.

XDADD "Minitab" "[myproj.mpj]pulse" "C1";

REPLACE C2;

PERFORM 2;

COMMAND "LET C2=4*C1".

Minitab Statistical Software Dynamic Data Exchange

When another program supports Edit > Paste Link, you can find out the application, topic, and item names by

copying a range from the other application and pasting it into the worksheet in Minitab. The type of link established

by XDADD is often referred to as a hot link.

You can use XDADD in global macros and exec macros, but not in local macros.

Data

DATA

Specifies whether to add (append) the data to the end of the columns that you specify or to replace the entire

contents of the columns in the worksheet with new data from the item you specify in the XDADD or XDGET

command. The column argument specifies the starting, or anchor column for the data in the item. When the item

has more than one column, other columns are also included.

APPEND [C]

Specifies to add the data to the end of the columns that you specify.

REPLACE [C] (default)

Specifies to replace the entire contents of the columns in the worksheet with new data from the item you

specify in the XDADD or XDGET command.

Link status

ACTIVE

Specifies to open the new link in the active state. An active link starts receiving data from the other application

immediately.

INACTIVE

Specifies to open the new link in the inactive state. An inactive link does not receive data until you activate it.

Command arguments

PERFORM K

Specifies the action to take on each occurrence of a data transfer from the server application. The argument values

and associated actions are shown in the following table.

DescriptionValue of K

Update data and execute commands.0

Update data only.1

Execute commands only.2

COMMAND "command"

Specifies one Minitab command to execute upon each occurrence of a data transfer from the server application.

The command string can contain up to 128 characters (without line breaks) and include one Minitab command

or %macro. Do not use a period at the end.

Use a %macro or the dialog box if you need to use multiple commands.

If the command includes single quotation marks ('), enclose the single quotation marks with double quotation

marks ("). You can only use a single COMMAND subcommand for each link.

Minitab Statistical Software Dynamic Data Exchange

This subcommand works differently than the Commands field in the Add New Links dialog box.

Link priority

PRIORITY K

Specifies the priority of the link relative to other links. The priority can be any integer from 1 (the highest priority)

to 32 (the lowest priority). Priority matters when more than one link attempts a transfer at the same time, or when

several queued transactions are waiting for processing. Minitab always completes transfers on links that have

higher priority first, then processes data from links with lower priority. Links with the same priority are transferred

on a first-in first-out basis. The default priority is 16.

XDDEACTIVATE: Session command for deactivating

a client link

XDDEACTIVATE "application" "topic" "item"

Allows you to pause (deactivate) activity on a client link. When you deactivate a link, Minitab ignores/discards any

queued transactions or incoming transactions until you use XDACTIVATE on page 57 to reactivate the link.

You can use XDDEACTIVATE in execs and global macros, but not in local macros.

You can also pause activity globally.

application

Application is the name of a program that can participate in a DDE transaction. Usually, application is the

name of an EXE file that starts the program, without the .exe extension. For example, in the case of Minitab,

Mtb.exe starts the program, so for the application argument use Mtb.

topic

Topic is a name that depends on the type of application. In applications that use documents or data files,

the topic is often the name of the file. For example, the topic for a Microsoft Word document might be

mywork.doc. The file name can also include a path, as in c:\mywork\mywork.doc.

If one file can contain many documents or subwindows, the Topic can be the name of the file in brackets,

followed by the name of the document or window. For example, Minitab projects can contain many worksheets,

so a typical Topic would be [Minitab] worksheet 1.

Other applications have their own names for topics.

Tip It is a good idea to save files from other applications before establishing any links to or from them. If you establish a link

before saving the file, you may need to change the link name from something like "untitled" to a file name. Likewise, before

linking from Minitab, rename a new worksheet from the default of "Worksheet 1" to something meaningful, such as "First Quarter."

item

Item is a name that depends on the type of application. In Minitab, the item always specifies a row/column

location or rectangular area in the form R4C1:R4C2. You can use all of Minitab column 1 as an item by

specifying C1. A single cell (row 4, column 1) is R4C1. Most spreadsheets use close variants of the R4C1:R3C2

format for items.

Minitab Statistical Software Dynamic Data Exchange

XDEXEC: Session command for executing a

command in a remote application

XDEXEC "application" "command"

Allows you to execute a command in a remote application.

You can use XDEXEC in any type of macro.

application

The application argument is the DDE application name that is defined by the other application.

Application is the name of a program that can participate in a DDE transaction. Usually, application is the

name of an EXE file that starts the program, without the .exe extension.

command

If the other application allows more than one command per line, you can use more than one command in

the command string. If the server does not allow more than one command, consider writing a macro in the

server language, or using several XDEXEC commands in a row to achieve the same result. Use double quotation

marks inside the command string if single quotation marks are required in the remote application.

Note If the command that you want to execute already contains double quotation marks, then put the command and application

in single quotation marks. For example, because the syntax for executing an Excel macro requires double quotation marks, the

correct syntax is XDEXEC 'EXCEL' '[Run("macro")]'.

XDGET: Session command for performing a

one-time data transfer

XDGET "application" "topic" "item"

Allows you to do a one time data transfer (often referred to as a cold link). You can use XDGET in place of copying

and pasting or in any type of macro.

application

Application is the name of a program that can participate in a DDE transaction. Usually, application is the

name of an EXE file that starts the program, without the .exe extension. For example, in the case of Minitab,

Mtb.exe starts the program, so for the application argument use Mtb.

topic

Topic is a name that depends on the type of application. In applications that use documents or data files,

the topic is often the name of the file. For example, the topic for a Microsoft Word document might be

mywork.doc. The file name can also include a path, as in c:\mywork\mywork.doc.

If one file can contain many documents or subwindows, the Topic can be the name of the file in brackets,

followed by the name of the document or window. For example, Minitab projects can contain many worksheets,

so a typical Topic would be [Minitab] worksheet 1.

Minitab Statistical Software Dynamic Data Exchange

Other applications have their own names for topics.

Tip It is a good idea to save files from other applications before establishing any links to or from them. If you establish a link

before saving the file, you may need to change the link name from something like "untitled" to a file name. Likewise, before

linking from Minitab, rename a new worksheet from the default of "Worksheet 1" to something meaningful, such as "First Quarter."

item

Item is a name that depends on the type of application. In Minitab, the item always specifies a row/column

location or rectangular area in the form R4C1:R4C2. You can use all of Minitab column 1 as an item by

specifying C1. A single cell (row 4, column 1) is R4C1. Most spreadsheets use close variants of the R4C1:R3C2

format for items.

APPEND and REPLACE

Specifies whether to add (append) the data to the end of the columns that you specify or to replace the entire contents

of the columns in the worksheet with new data from the item you specify in the XDADD or XDGET command. The

column argument specifies the starting, or anchor column for the data in the item. When the item has more than one

column, other columns are also included.

APPEND [C]

Specifies to add the data to the end of the columns that you specify.

REPLACE [C] (default)

Specifies to replace the entire contents of the columns in the worksheet with new data from the item you specify

in the XDADD or XDGET command.

XDREMOVE: Session command for deleting an

established link

XDREMOVE "application" "topic" "item"

Deletes an established client link.

This command deletes the specified client link immediately when Minitab accepts the command. When a link is

deleted, all existing queued requests are processed (that is transferred into the worksheet), but no new ones are

added.

You can use XDREMOVE in execs and global macros, but not in local macros.

application

Application is the name of a program that can participate in a DDE transaction. Usually, application is the

name of an EXE file that starts the program, without the .exe extension. For example, in the case of Minitab,

Mtb.exe starts the program, so for the application argument use Mtb.

topic

Topic is a name that depends on the type of application. In applications that use documents or data files,

the topic is often the name of the file. For example, the topic for a Microsoft Word document might be

mywork.doc. The file name can also include a path, as in c:\mywork\mywork.doc.

If one file can contain many documents or subwindows, the Topic can be the name of the file in brackets,

followed by the name of the document or window. For example, Minitab projects can contain many worksheets,

so a typical Topic would be [Minitab] worksheet 1.

Minitab Statistical Software Dynamic Data Exchange

Other applications have their own names for topics.

Tip It is a good idea to save files from other applications before establishing any links to or from them. If you establish a link

before saving the file, you may need to change the link name from something like "untitled" to a file name. Likewise, before

linking from Minitab, rename a new worksheet from the default of "Worksheet 1" to something meaningful, such as "First Quarter."

item

Item is a name that depends on the type of application. In Minitab, the item always specifies a row/column

location or rectangular area in the form R4C1:R4C2. You can use all of Minitab column 1 as an item by

specifying C1. A single cell (row 4, column 1) is R4C1. Most spreadsheets use close variants of the R4C1:R3C2

format for items.

Minitab Statistical Software Dynamic Data Exchange

Manipulating and Calculating Data

Calculator

Calculations

ADD: Session command for addition

ADD E...E E

Adds E and E and stores in E. E can be any column, any constant, or matrix. For columns and constants, ADD allows

up to 50 arguments. If any element of a row is missing, the result is set to missing *. If the operation is impossible,

the result is also set to missing.

EIGEN: Session command for calculating eigenvalues

EIGEN M C [M]

Calculates eigenvalues (also called characteristic values or latent roots) and eigenvectors for a symmetric matrix.

The eigenvalues are stored in decreasing order of magnitude down the column. The eigenvectors are stored as

columns of the matrix. The first column corresponds to the first eigenvalue (largest magnitude), the second column

to the second eigenvalue, and so on.

LAG: Session command for calculating the lags of a column

LAG [K] C C

Calculates the lags of a column and stores them in a new column.

Moves the row elements of a column down K rows, where K is the lag specified, storing the result in a new column

of the same length. There will be K missing value symbols, *, at the top of the output column. The output column

has the same number of rows as the input column, so the last K values from the input column are not lagged. If

K is omitted, then K = 1 is used.

If C1 contains Z1, Z2, ..., Zn, then LAG K C1 C2 puts asterisks (*) into rows 1 through K of C2, and puts Z

– Z

i-k

into row i, K + 1 < i < n.

LET: Session command for correcting a number in a worksheet or performing arithmetic

LET C [K] = K

Use to correct a number in the worksheet. You can change a single number with the LET command. For example,

LET C1[5] = 28.6 puts 28.6 into row 5 of C1. The rest of C1 remains the same.

Minitab Statistical Software Manipulating and Calculating Data

LET = expression

Use to perform arithmetic with an algebraic expression. The expression can contain arithmetic operations,

comparison operations, logical operations, and functions. The functions that you can use with LET are described

below.

Note In an expression, you cannot use a hyphen to specify a range of columns or constants. For example, Minitab would interpret

C1-C4 as C1 minus C4 and 1-10 as 1 minus 10.

You can also access an individual row in a column. For arguments, you can use columns, stored constants, or

numbers. You cannot use matrices. You cannot use extra text on any session command, including LET, except

after the # symbol.

For arithmetic operations, if any element of a row is missing, the result is set to missing. If an operation is impossible,

such as division by zero, the result is set to missing. You can have up to nine nested parentheses.

Examples of LET

LET C1 = (C2 + C3)*10 - 60

LET C1 = C1 - MEAN(C1)

LET K1 = 5.3

LET K2 = MEAN(C10)/STDEV(C1)

LET C5 = (C1 < 5)

LET K2 = C1[28]

LET C1[28] = 2.35

LET C5[15] = "blue"

Arithmetic functions to use with LET

CEIL (E, K)

Rounds numbers up. In the first argument, specify the number you want rounded. In the second argument, specify

the number of decimals to round to. If K = 0 (the default), the number is rounded to the nearest integer greater

than or equal to the number. If K > 0, the number is rounded up to the specified number of decimal places after

the decimal point. If K < 0, the number is rounded up to 1- the specified number of place values to the left of the

decimal point.

For example:

CEILING (2.136, 0) equals 3

CEILING (2.136, 1) equals 2.2

CEILING (2.136, 2) equals 2.14

CEILING (–2.136, 1) equals –2.1

CEILING (253.6, –1) equals 260

CEILING (253.6, –2) equals 300

Minitab Statistical Software Manipulating and Calculating Data

COMBINATIONS (E, E)

Calculates the number of combinations of n items chosen k at a time. In the first argument, specify the number

of items. In the second argument, specify the number to choose. The number of items must be greater than or

equal to 1, and the number to choose must be greater than or equal to 0. Arguments can be columns or constants.

Missing values are not allowed.

FACTORIAL (E)

Calculates the factorial of a number, the product of all the consecutive integers from 1 to the number, inclusive.

The value of the number must be greater than or equal to 0. Missing values are not allowed.

FLOOR (E, K)

Rounds numbers down. In the first argument, specify the number you want rounded. In the second argument,

specify the number of decimals to round to. If K = 0 (the default), the number is rounded to the nearest integer

less than or equal to the number. If K > 0, the number is rounded down to the specified number of decimal places

after the decimal point. If K < 0, the number is rounded down to 1- the specified number of place values to the

left of the decimal point.

For example:

FLOOR (2.136, 0) equals 2

FLOOR (2.136, 1) equals 2.1

FLOOR (2.136, 2) equals 2.13

FLOOR (–2.136, 1) equals –2.2

FLOOR (253.6, –1) equals 250

FLOOR (253.6, –2) equals 200

FTC (E)

Performs the Freeman Tukey transformation to stabilize variance for Poisson data. Requires one argument, which

must be a column or stored constant that contains nonnegative integers.

FTP (E, E)

Performs the Freeman Tukey transformation to stabilize variance for binomial data. Requires one argument for

number of trials and one for number of successes. Each argument can be a column or stored constant. Trials must

be a positive integer, and successes must be an integer between 0 and n inclusive.

GAMMA (E)

Calculates the gamma function where E is the specified shape parameter (the number you want to take the function

of).

IGAMMA (E, E)

Calculates the incomplete gamma function. In the first argument, specify the upper limit of the integral. In the

second argument, specify the shape parameter (the number you want to take the function of).

LNGAMMA (E)

Calculates the natural log of the gamma function. E is the shape parameter.

MOD (E, E)

Stores the remainder after a number is divided by a divisor. In the first argument, specify the number. In the

second argument, specify the divisor. Minitab calculates the value using the formula: m – (n * FLOOR (m/n)) where

m is a number and n is the divisor.

Minitab Statistical Software Manipulating and Calculating Data

PERMUTATIONS (E, E)

Calculates the number of permutations of n things taken k at a time. In the first argument, specify the number of

items. In the second argument, specify the number to choose. The number of items must be greater than or equal

to 1, and the number to choose must be greater than or equal to 0. Missing values are not allowed.

Column functions to use with LET

DIFFERENCES (C, K)

Calculates row-by-row differences between the numeric values in a column. In the first argument, specify the

column. Minitab subtracts from each row the element K rows above, where K is the lag you specify, and stores

the differences in a new column. If you don't specify a value for lag, the differences are computed between

consecutive rows (lag = 1). The first K rows of the new column will contain the missing value symbol, *.

LAG (C, K)

Moves the row elements of a column down K rows, storing the result in a new column of the same length. If no

value is specified, the default lag (K =1) is used. There will be K missing value symbols, *, at the top of the output

column. The output column has the same number of rows as the input column, so the last value from the input

column is not lagged.

NSCORES (C)

Calculates and stores normal scores. This command is used mainly to produce normal probability plots and

perform various tests. Here is an example of normal scores.

LET C2 = NSCORES(C1)

Loosely speaking, -1.18 is the smallest value you would expect to get if you took a sample of size 5 from a standard

normal (i.e., the expected value of the first order statistic), and -0.50 is the second smallest value you would expect

to get (i.e., the expected value of the second order statistic), and so on.

C2C1

0.01.1

-1.180.1

1.182.3

0.501.8

-0.500.9

Minitab does not calculate the expected values of the order statistics exactly, but uses percentage points as an

approximation. Thus, if there are n data values, Minitab puts f - 1[ (i - 3 / 8) / (n + 1 / 4)] next to the ith smallest

data value, where f -1(x) is the inverse cumulative distribution function of the standard normal.

If several observations are equal, they are all given the same normal score. This is calculated using the average

of their ranks. The command NORMPLOT provides additional information. The output from NORMTEST includes

a normal probability plot, relevant statistics about the data, and test statistics useful for testing a hypothesis of

normality.

RANK (C)

Calculates and stores the ranks of the input column. Assigns the numeral 1 to the smallest value, the numeral 2

to the second smallest value, the numeral 3 to the third smallest value, and so on. Ties are assigned the average

rank.

Note RANK works only with numeric columns.

The following command language ranks the values in C1 and puts the ranked values in C2.

RANK C1 C2

Minitab Statistical Software Manipulating and Calculating Data

Before ranking

0.5

1.0

1.5

1.0

2.0

0.0

After ranking

C2C1

2.00.5

3.51.0

5.01.5

3.51.0

6.02.0

1.00.0

SORT (C)

Sorts the numerical values in a column in ascending order. Specify the column. Data must be numerical.

Date/time functions to use with LET

CTIME ()

Returns the current time, for example, 9:26:20 AM.

Note If stored in a column, the result is in a date/time format. If stored in a constant, the result is the numeric representation of the

date/time value. Currently, stored constants in Minitab do not have date/time formats.

DATE ("text")

DATE (E)

Returns the date portion corresponding to the argument. The argument should be a text string, such as "3/6/99

10:23", a column, or a stored constant. Text columns and constants should be in one of the Default date/time

formats on page 1140.

Note If stored in a column, the result is in a date/time format. If stored in a constant, the result is the numeric representation of the

date/time value. Currently, stored constants in Minitab do not have date/time formats.

ELAPSED (E)

Returns the elapsed time, given the start and end times. Enter the column with the end times minus the column

with the start times. The columns or values must be in numeric or date/time format. The elapsed time in the

output is in minutes and seconds (mm:ss) if the maximum value of the output column is less than one hour; the

elapsed time in the output is in hours, minutes, and seconds (hh:mm:ss) if the maximum of the output column is

an hour or more.

Minitab Statistical Software Manipulating and Calculating Data

NETWORKDAYS (E, E, [E])

Returns the number of workdays between two dates, inclusive. Specify the start date and end date in the first two

arguments. You can enter a column of dates. You can also specify a column of holidays to skip for the optional

third argument. If the dates include time portions, Minitab ignores the times. You can also enter single dates in

double quotes with the additional use of DATE. For example, to find out the number of workdays between 1/1/09

and 1/31/09, use LET c1 = NETWORKDAYS(date("1/1/09"), date("1/31/09")). Text columns and constants should

be in one of the Default date/time formats on page 1140.

NOW ()

Returns the current date and time, for example, 3/8/2003 9:24.

Note If stored in a column, the result is in a date/time format. If stored in a constant, the result is the numeric representation of the

date/time value. Currently, stored constants in Minitab do not have date/time formats.

TIME ("text")

TIME (E)

Returns the time portion corresponding to the argument. The argument should be a text string, such as "3/6/99

10:23", a column, or a stored constant. Text columns and constants should be in one of the Default date/time

formats on page 1140.

Note If stored in a column, the result is in a date/time format. If stored in a constant, the result is the numeric representation of the

date/time value. Currently, stored constants in Minitab do not have date/time formats.

TODAY ()

Returns today's date, for example, 3/8/2003.

Note If stored in a column, the result is in a date/time format. If stored in a constant, the result is the numeric representation of the

date/time value. Currently, stored constants in Minitab do not have date/time formats.

WHEN ("text")

WHEN (E)

Returns the date and time corresponding to the argument. The argument should be a text string, such as "3/6/99

10:23", or a column or stored constant. Text columns and constants should be in one of the Default date/time

formats on page 1140.

Note If stored in a column, the result is in a date/time format. If stored in a constant, the result is the numeric representation of the

date/time value. Currently, stored constants in Minitab do not have date/time formats.

WDAYS ("text", K, ["text"])

WDAYS (E, E, [E] )

Offsets the date by the given number of workdays. Enter the start date and the number of workdays to add to

the date. Minitab returns the date that is the number of working days (defined as Monday through Friday) from

the start date you specify. You can also choose to skip holidays by entering a date or column or dates for the

optional third argument. When you enter single dates in double quotes, you need to also use DATE. For example,

to find out the date of tenth workday after 1/5/09, use LET c1 = WORKDAYS(date("1/5/09"),10). Text columns and

constants should be in one of the Default date/time formats on page 1140.

Note If stored in a column, the result is in a date/time format. If stored in a constant, the result is the numeric representation of the

date/time value. Currently, stored constants in Minitab do not have date/time formats.

Minitab Statistical Software Manipulating and Calculating Data

Logical functions to use with LET

ANY (E, K...)

Returns a 1 if a value equals any value from a set of values, returns a 0 otherwise. Use to flag specified values in

a column. In the first argument, specify the column. In the subsequent arguments, specify the values for which to

return a 1.

IF (C <>=K, "text", ["text"])

IF (C<>=C, C, [C])

Chooses which of two values to return based on whether a condition is true or false. Conditions can be any

numerical or logical expressions. For the first argument, specify the column and the condition using >, <, or =.

For the second argument, specify the value to return if the condition is true. The third argument is optional and

allows you to specify a value to return if the condition is false. If nothing is specified, Minitab returns a missing

value. For example, IF(c1 = 1, "male", "female") returns "male" for values in c1 equal to 1 and returns "female" for

the other values in c1.

IF (C<>=K,"text", C<>= "text"...["text"])

IF (C<>=K, C, C<>= C...[C])

Chooses a value to return for each of multiple conditions evaluated sequentially. Conditions can be any numerical

or logical expressions. For the first argument, specify the column and the condition using >, <, or =. For the second

argument, specify the value to return if the condition is true. You can enter multiple conditions and values. All

values must be the same data type (numeric or text). Minitab returns the value corresponding to the first true

condition, working from left to right. The final argument is optional and allows you to specify a value to return if

all the conditions are false. If nothing is specified, Minitab returns a missing value.

For example, IF(c1 <= 2, "low", c1 <=4, "medium", "high") returns "low" for c1 values less than or equal to 2,

"medium" for values less than or equal to 4 but greater than 2, and "high" for the remaining data in c1.

Statistics functions to use with LET

COUNT (C)

Counts the number of nonmissing and missing values in a column.

GMEAN (C)

Calculates the geometric mean, the nth root of the product of the n values. All values must be greater than 0.

MAXIMUM (C)

Calculates the maximum value in a column. Missing values are omitted from the calculation.

MEAN (C)

Calculates the arithmetic mean of all the values in a column. Missing values are omitted from the calculation.

MEDIAN (C)

Calculates the median of the values in a column. Missing values are omitted from the calculation.

MINIMUM (C)

Identifies the minimum value in a column. Missing values are omitted from the calculation.

N (C)

Counts the number of nonmissing values in a column.

NMISS (C)

Counts the number of missing values in a column.

Minitab Statistical Software Manipulating and Calculating Data

NSCORES (C)

Calculates normal scores.

PERC (C, E)

Calculates the sample percentiles For the first argument, enter the column of data. For the second argument,

enter the column or constant specifying the desired percentiles, from 0 to 1. Missing values are ignored. For

example, to find the first quartile (25

percentile) of a column of data, enter the column number and the percentile

0.25.

RANGE (C)

Calculates the range of the values in a column. Missing values are omitted from the calculation.

SSQ (C)

Calculates the uncorrected sum of squares for a column. Missing values are omitted from the calculations.

STDEV (C)

Calculates the standard deviation for all the values in a column. Missing values are omitted from the calculation.

SUM (C)

Adds all the values in a column. Missing values are omitted from the calculation.

Text functions to use with LET

CLEAN ("text")

CLEAN (E)

Removes all nonprintable characters. Specify the text, column, or stored constant.

CONCATENATE ("text", "text"...)

CONCATENATE (E, E...)

Combines two or more columns or values side-by-side. Specify the text, columns, or stored constants. Minitab

treats numbers appearing in a text column (as in a street address or date/time) as text characters and will convert

numeric values to text.

FIND (E, E, [E])

Identifies the starting position of a string of text within another string of text. For the first argument, specify the

text string or the column of text that you want to find. For the second argument, specify the text value or column

of text to search. FIND is case-sensitive.

By default, Minitab searches from the first position of each text entry. You can also specify another starting position

(the location in the string from where to start the search) by inserting a number for K, the optional third argument.

For example, if c1 contains 234b75, FIND("b7",c1) returns 4, because b7 begins at the 4

position in the text.

Minitab returns a missing value * if the string of text is not found.

FIXED (E, K, [1])

Rounds a number to the specified number of decimals and converts it to text with or without commas. For the

first argument, specify the number or the column of numeric data you want to convert. For the second argument,

specify the number of decimals to retain. If K = 1, Minitab rounds to the nearest tenth. If K = 0 Minitab rounds

to the nearest integer. If K = –1, Minitab rounds to a multiple of ten. If you don't enter a second argument, Minitab

rounds to 2 decimal places by default.

Minitab Statistical Software Manipulating and Calculating Data

Minitab inserts commas to separate some place values (hundreds and thousands, hundred-thousands and millions,

etc.) in the converted text. If you don't want the commas to appear, enter a value of 1 for the optional third

argument.

HTOD (E)

Converts hexadecimal values to their equivalent decimal form. The original hexadecimal data must be in text

format.

ITEM ("text", E, [E])

ITEM E, E, [E])

Extracts the nth word from a string of text. For the first argument, specify the text or column of text. For the second

argument, specify the position of the word to extract.

By default, one or more spaces define where each word begins and ends. If you want to specify other criteria for

determining the separation between the words, such as a comma, specify the separator using the optional third

argument.

The ITEM function is similar to the WORD function, except that ITEM extracts empty text that occurs between

consecutive separators (such as ,,) while WORD ignores the empty string and extracts the text that follows

consecutive separators.

LAG (C, [K])

Copies the text in the input column to the storage column, moving each value down by the numbers of rows you

specify. For the first argument, specify the input column. For the second argument, specify the number of rows

the text values should be moved down. By default, if no value is specified, Minitab moves the text values down

one row (lag = 1).

LEFT ("text", E)

LEFT (C, E)

Returns the specified number of characters from the beginning of a string of text. For the first argument, specify

the text or the column of text values. For the second argument, indicate how many characters from the left you

want to retain.

LEN ("text")

LEN (C)

Identifies the number of characters in a string of text. Specify the text or the column of text values.

LOWER ("text")

LOWER (C)

Converts all letters to lowercase. Specify the text or the column of text values.

MID ("text", E, [E])

MID (C, E, [E])

Returns the characters from the middle part of a string of text, given the starting position and the length. For the

first argument, specify the text or the column of text values. For the second argument, specify the position of the

first character to return.

You can specify an integer for the optional third argument to limit the number of characters that Minitab returns.

If you don't enter the third argument, Minitab returns all the characters that follow the starting position.

Minitab Statistical Software Manipulating and Calculating Data

PAD ("text", E)

PAD (C, E)

Pads text with trailing spaces. For first argument, specify the text or the column of text values. For the second

argument, enter the total number of characters needed for the text and the trailing spaces. The padding is indicated

by total number of characters minus the number of characters in the text.

PROPER ("text")

PROPER (C)

Capitalizes the first letter in each word and converts all other characters to lowercase. Specify the text or the

column of text values.

REPT ("text", E)

REPT (C, E)

Repeats text a given number of times. For the first argument, specify the text or the column of text. For the second

argument, specify how many times to repeat the text.

REPLACE ("text", E, E, E)

REPLACE (C, E, E, E)

Replaces a substring of text within a string of text. For the first argument, specify the original text or column of

text values. For the second argument, specify the position of the first character to replace. For the third argument,

specify how many characters to replace. For the last argument, enter the new text that you want to replace the

old text with.

RIGHT ("text", E)

RIGHT (C, E)

Returns the specified number of characters from the end of a string of text. For the first argument, specify the

text or the column of text values. For the second argument, indicate how many characters from the right you want

to retain.

SEARCH (E, E, E)

Identifies the starting position of a string of text within another string of text. For the first argument, specify the

text string you want to find. For the second argument, specify the column of text to search. FIND is case-sensitive

(distinguishes between b and B).

By default, Minitab searches from the first position of each text entry. You can also specify another starting position

(the location in the string from where to start the search) by inserting a number for K, the optional third argument

of the function.

SEARCH is similar to FIND, except that SEARCH is not case-sensitive (does not distinguish between b and B).

SUBSTITUTE ("text", E, E, [E])

SUBSTITUTE (C, E, E, [E])

Replaces existing text with new text and allows you to specify which occurrence of the old text you want to replace

if the text occurs more than once in a single entry. For the first argument, specify the text or column of text. For

the second argument, specify the text that you want to replace. For the third argument, specify the new text you

want to substitute. For the last (optional) argument, you can specify which occurrence of the old text you want

to replace, if the old text occurs more than once. For example, If c1 contains 600 Pine Lane SUBSTITUTE (c1, "0",

"2", 1) returns 620 Pine Lane.

TEXT (E)

Converts a numeric value to text. Converting numeric values to text allows you to edit and manipulate the values

using text manipulation functions. Specify the number or the column of numeric values.

Minitab Statistical Software Manipulating and Calculating Data

TRIM ("text")

TRIM (C)

Removes all spaces except single spaces between words. Specify the text or column of text.

WORD ("text", E, [E])

WORD (C, E, [E]

Extracts the n

word from a string of text. For the first argument, specify the text or column of text. For the second

argument, specify the position of the word to extract.

By default, one or more spaces define where each word begins and ends. If you want to specify other criteria for

determining the separation between the words, such as a comma, specify the separator using the optional third

argument.

The WORD function is similar to the ITEM function, except that ITEM extracts the empty text that occurs between

consecutive separators (such as the comma and space) while WORD ignores the empty string and extracts the

text that follows the consecutive separators.

Trigonometry functions to use with LET

ACSH (E)

Calculates the hyperbolic arccosine of a value. Specify the value or the column of values.

ASNH (E)

Calculates the hyperbolic arcsine of a value. Specify the value or the column of values.

ATNH (E)

Calculates the hyperbolic arctangent of a value. Specify the value or the column of values.

COSH (E)

Calculates the hyperbolic cosine of a value. Specify the value or the column of values.

DEGREES (E)

Changes radians to degrees. Specify the value or the column of values.

RADIANS (E)

Changes degrees to radians. Specify the value or the column of values.

SINH (E)

Calculates the hyperbolic sine of a value. Specify the value or the column of values.

TANH (E)

Calculates the hyperbolic tangent of a value. Specify the value or the column of values.

Minitab Statistical Software Manipulating and Calculating Data

Constants functions to use with LET

E ( )

Calculates e = 2.71828...

MISS ( )

Returns the * = missing value symbol in the specified column.

PI ( )

Calculates pi = 3.14159...

Row statistics functions to use with LET

RCOUNT (E, E...)

Counts the number of missing and nonmissing values in the row. You cannot use a hyphen to specify a range of

columns. For example, Minitab would interpret C1-C4 as C1 minus C4.

However, you can use the session command RCOUNT on page 77 to count the number of values in each row

while specifying a range of columns, such as C1-C4.

RMAXIMUM (E, E...)

Identifies the maximum value in each row. Missing values are omitted from the calculation. You cannot use a

hyphen to specify a range of columns. For example, Minitab would interpret C1-C4 as C1 minus C4.

However, you can use the session command RMAXIMUM on page 77 to calculate the maximum value in each

row while specifying a range of columns, such as C1-C4.

RMEAN (E, E...)

Calculates the arithmetic mean of the values in each row. Missing values are omitted from the calculation. You

cannot use a hyphen to specify a range of columns. For example, Minitab would interpret C1-C4 as C1 minus C4.

However, you can use the session command RMEAN on page 78 to calculate the mean of each row while specifying

a range of columns, such as C1-C4.

RMEDIAN (E, E...)

Identifies the median of the values in each row. Missing values are omitted from the calculation. You cannot use

a hyphen to specify a range of columns. For example, Minitab would interpret C1-C4 as C1 minus C4.

However, you can use the session command RMEDIAN on page 78 to calculate the median of each row while

specifying a range of columns, such as C1-C4.

RMINIMUM (E, E...)

Identifies the minimum value in each row. Missing values are omitted from the calculation. You cannot use a

hyphen to specify a range of columns. For example, Minitab would interpret C1-C4 as C1 minus C4.

However, you can use the session command RMINIMUM on page 78 to calculate the minimum value in each row

while specifying a range of columns, such as C1-C4.

RN (E, E...)

Counts the number of nonmissing values in the row. You cannot use a hyphen to specify a range of columns. For

example, Minitab would interpret C1-C4 as C1 minus C4.

Minitab Statistical Software Manipulating and Calculating Data

However, you can use the session command RN on page 78 to count the number of nonmissing values in each

row while specifying a range of columns, such as C1-C4.

RNMISS (E, E...)

Counts the number of missing entries in the row. You cannot use a hyphen to specify a range of columns. For

example, Minitab would interpret C1-C4 as C1 minus C4.

However, you can use the session command RNMISS on page 78 to count the number of missing values in each

row while specifying a range of columns, such as C1-C4.

RRANGE (E, E...)

Calculates the range of values in each row. Missing values are omitted from the calculation. You cannot use a

hyphen to specify a range of columns. For example, Minitab would interpret C1-C4 as C1 minus C4.

However, you can use the session command RRANGE on page 79 to calculate the range of each row while

specifying a range of columns, such as C1-C4.

RSSQ (E, E...)

Calculates the uncorrected sum of squares of the values in each row. Missing values are omitted from the calculation.

You cannot use a hyphen to specify a range of columns. For example, Minitab would interpret C1-C4 as C1 minus

C4.

However, you can use the session command RSSQ on page 79 to calculate the uncorrected sum of squares for

each row while specifying a range of columns, such as C1-C4.

RSTDEV (E, E...)

Calculates the standard deviation of the values in each row. Missing values are omitted from the calculation. You

cannot use a hyphen to specify a range of columns. For example, Minitab would interpret C1-C4 as C1 minus C4.

However, you can use the session command RSTDEV on page 79 to calculate the standard deviation of each row

while specifying a range of columns, such as C1-C4.

RSUM (E, E...)

Adds the values in each row. Missing values are omitted from the calculation. You cannot use a hyphen to specify

a range of columns. For example, Minitab would interpret C1-C4 as C1 minus C4.

However, you can use the session command RSUM on page 79 to add values in each row while specifying a range

of columns, such as C1-C4.

MULTIPLY: Session command for multiplication

MULTIPLY E...E E

Note This functionality is available only as a session command, and not in a menu.

Multiplies E by E and stores the result in E. E can be any column, any constant, or any matrix. For columns and

constants, MULTIPLY allows up to 50 arguments. If any element of a row is missing, the result is set to missing. If

the operation is impossible, the result is also set to missing, *.

Minitab Statistical Software Manipulating and Calculating Data

Column Statistics

COUNT: Session command for counting the number of values in a column

COUNT C [K]

Counts and optionally stores the number of nonmissing and missing values in a column. (Equivalent to N total

in the menu command dialog box.)

You can also use this command as a function with LET on page 63.

MEAN: Session command for calculating the arithmetic mean of a column

MEAN

Calculates the arithmetic mean of all the values in a column and optionally stores the result. Missing values are

omitted from the calculation.

You can also use this command as a function with LET on page 63.

MEDIAN: Session command for identifying the median of a column

MEDIAN C [K]

Identifies the median of the values in a column and optionally stores the result. Missing values are omitted from

the calculation.

You can also use this command as a function with LET on page 63.

N: Session command for counting the nonmissing values in a column

N C [K]

Counts and optionally stores the number of nonmissing values in a column. (Equivalent to N nonmissing in the

menu command dialog box.)

You can also use this command as a function with LET on page 63.

NMISS: Session command for counting the missing values in a column

NMISS C [K]

Counts and optionally stores the number of missing values in a column. (Equivalent to N missing in the menu

command dialog box.)

You can also use this command as a function with LET on page 63.

Minitab Statistical Software Manipulating and Calculating Data

RANGE: Session command for calculating a range of values in a column

RANGE C [K]

Calculates the range of values in a column and optionally stores the result. Missing values are omitted from the

calculation.

You can also use this command as a function with LET on page 63.

SSQ: Session command for calculating the uncorrected sum of squares

SSQ C [K]

Calculates the uncorrected sum of squares for a column and optionally stores the result. Missing values are omitted

from the calculations.

You can also use this command as a function with LET on page 63.

STDEV: Session command for calculating the standard deviation of all the values in a column

STDEV C [K]

Calculates the standard deviation for all the values in a column and optionally stores the result. Missing values

are omitted from the calculation.

You can also use this command as a function with LET on page 63.

SUM: Session command for adding the values in a column

SUM C [K]

Adds all the values in a column and optionally stores the result. Missing values are omitted from the calculation.

You can also use this command as a function with LET on page 63.

Row Statistics

RCOUNT: Session command for counting missing and nonmissing values in a row

RCOUNT E...E C

Counts and stores the number of missing and nonmissing values in the row. (Equivalent to N total in the menu

command dialog box.)

Note You can also use this command as a function with LET on page 63. If you use this command as an argument of LET, you cannot

use a hyphen to specify a range of values. For example, Minitab would interpret C1-C4 as C1 minus C4.

RMAXIMUM: Session command for identifying the maximum value in each row

RMAXIMUM E...E C

Identifies and stores the maximum value in each row. Missing values are omitted from the calculation.

Minitab Statistical Software Manipulating and Calculating Data

You can also use this command as a function with LET on page 63. If you use this command as an argument of

LET, you cannot use a hyphen to specify a range of values. For example, Minitab would interpret C1-C4 as C1

minus C4.

RMINIMUM: Session command for identifying the minimum value in each row

RMINIMUM E...E C

Identifies and stores the minimum value in each row. Missing values are omitted from the calculation.

You can also use this command as a function with LET on page 63. If you use this command as an argument of

LET, you cannot use a hyphen to specify a range of values. For example, Minitab would interpret C1-C4 as C1

minus C4.

RMEAN: Session command for calculating the arithmetic mean in each row

RMEAN

Calculates and stores the arithmetic mean of the values in each row. Missing values are omitted from the calculation.

You can also use this command as a function with LET on page 63. If you use this command as an argument of

LET, you cannot use a hyphen to specify a range of values. For example, Minitab would interpret C1-C4 as C1

minus C4.

RMEDIAN: Session command for identifying the median in each row

RMEDIAN

Identifies and stores the median of the values in each row. Missing values are omitted from the calculation.

You can also use this command as a function with LET on page 63. If you use this command as an argument of

LET, you cannot use a hyphen to specify a range of values. For example, Minitab would interpret C1-C4 as C1

minus C4.

RN: The session command for counting the nonmissing values in a row

RN E...E C

Counts and stores the number of nonmissing values in the row. (Equivalent to N nonmissing in the menu command

dialog box.)

You can also use this command as a function with LET on page 63. If you use this command as an argument of

LET, you cannot use a hyphen to specify a range of values. For example, Minitab would interpret C1-C4 as C1

minus C4.

RNMISS: Session command for counting the missing values in a row

RNMISS E...E C

Counts and stores the number of missing entries in the row. (Equivalent to N missing in the menu command

dialog box.)

Minitab Statistical Software Manipulating and Calculating Data

You can also use this command as a function with LET on page 63. If you use this command as an argument of

LET, you cannot use a hyphen to specify a range of values. For example, Minitab would interpret C1-C4 as C1

minus C4.

RRANGE: Session command for calculating the range in each row

RRANGE E...E C

Calculates and stores the range of values in each row. Missing values are omitted from the calculation.

You can also use this command as a function with LET on page 63. If you use this command as an argument of

LET, you cannot use a hyphen to specify a range of values. For example, Minitab would interpret C1-C4 as C1

minus C4.

RSSQ: Session command for calculating the uncorrected sum of squares

RSSQ E...E C

Calculates and stores the uncorrected sum of squares of the values in each row. Missing values are omitted from

the calculation.

You can also use this command as a function with LET on page 63. If you use this command as an argument of

LET, you cannot use a hyphen to specify a range of values. For example, Minitab would interpret C1-C4 as C1

minus C4.

RSTDEV: Session command for calculating the standard deviation in each row

RSTDEV E...E C

Calculates and stores the standard deviation of the values in each row. Missing values are omitted from the

calculation.

You can also use this command as a function with LET on page 63. If you use this command as an argument of

LET, you cannot use a hyphen to specify a range of values. For example, Minitab would interpret C1-C4 as C1

minus C4.

RSUM: Session command for adding the values in each row

RSUM E...E C

Adds and stores the values in each row. Missing values are omitted from the calculation.

You can also use this command as a function with LET on page 63. If you use this command as an argument of

LET, you cannot use a hyphen to specify a range of values. For example, Minitab would interpret C1-C4 as C1

minus C4.

Minitab Statistical Software Manipulating and Calculating Data

Standardize

CENTER: Session command for centering data

CENTER C...C, C...C

Centers the data in C...C and stores the results in C...C. Allows you to center and scale columns of data.

When you do not include subcommands, Minitab transforms each input column by subtracting its mean and then

dividing by its standard deviation. This is often called standardizing a variable.

LOCATION [K...K]

When you do not include Ks, Minitab transforms each column by subtracting its mean. When you specify

one K, Minitab subtracts that value from each column. Otherwise, you must list one K for each column to be

centered. Then Minitab subtracts each K from the corresponding column.

SCALE [K...K]

When you use LOCATION, Minitab first subtracts the location. When you do not specify Ks on SCALE, Minitab

divides each column by its standard deviation. When you specify one K, Minitab divides each column by K.

Otherwise, you must list one K for each column to be centered. Then Minitab divides each column by the

corresponding K.

You must specify K as greater than 0.

MINMAX [K K]

When you do not specify Ks, Minitab transforms all columns (linearly) to have minimum –1 and maximum

+1. When you specify both Ks, Minitab transforms all columns (linearly) to have the first K as minimum and

the second K as maximum.

Make Patterned Data

SET: Session command for entering data into a column

SET C

Inputs data, from the keyboard or from an ASCII file, into one column.

If there is any data in the column you list, SET erases that data and replaces it with the new data you enter. SET

is especially useful for entering data that follow a pattern, such as the numbers 1 through 10, into a column. For

more information, go to Entering patterned data for the SET session command on page 1143.

If you execute SET from the menu, the FORMAT and NOBS options are not available, the form of the data is

restricted, and data cannot be read from a file.

As a simple example, the following command language puts the numbers 2, 7, 9, 3.8, and 22 in column C7.

SET C7

2 7 9

3.8 22

END

FILE 'filename'

Enters data from the specified ASCII file. If the file has an extension other than DAT and/or if it is not in your

current directory, include the file name extension and the path within the quotation marks.

Minitab Statistical Software Manipulating and Calculating Data

FORMAT

The FORMAT subcommand for SET is similar to that for READ (for information, go to READ data into a

matrix on page 43 or READ data into columns on page 41). For example, the following command

language reads 10 numbers from each data line-the first from spaces 1 and 2, the second from spaces

3 and 4, and so on.

SET C1;

FILE "MYFILE";

FORMAT (10F2).

Text data can be input using the FORMAT subcommand. The field width can be up to 80 characters.

You cannot mix text and numeric data in a single column.

Here is an example that inputs four "words" using A format into C1.

SET C1;

FORMAT (A11, T16, A2).

ABCDEFGHIJK LM

abcdefghijk lm

END

For more information, go to Valid format items on page 1186.

NOBS K

Specifies the number of observations (rows) to be read. If an END on page 38 subcommand or end-of-file

is encountered before K observations are read, NOBS is ignored. NOBS is useful when you want to read

just the first portion of a file. NOBS is also useful when you prompt the user for information. For more

information, go to Prompting a user for information on page 1177.

SKIP K

Tells Minitab to skip K lines at the top of the data file before beginning to READ or SET data into the

file. This is most useful when you have one or more lines of text, such as column names and titles, at

the top of a data file that you want to import into Minitab.

DECIMAL "." or ","

Specifies a period or comma as the decimal separator.

TSET: Session command for creating data that follow complicated patterns

TSET

Using TSET, you can create data that follow more complicated patterns. You can also use TSET to import text files.

TSET is the same as SET except there is no through construct, and you list text constants, stated or stored, instead

of numbers. Repeat factors are integers, stated or stored. A text string must be enclosed in double quotes as in

LET.

As in SET, you can input data from a file in addition to typing it from the keyboard.

FILE "filename"

Enters data from the specified file. If the file has an extension other than DAT and/or if it is not in your current

directory, include the file name extension and the path within the quotation marks.

Minitab Statistical Software Manipulating and Calculating Data

Examples

This example contains text constants and repeat factors.

TSET C1

5("Red" "Green") 2("Yellow") 3("Green")

END

LET K1 = "Low"

LET K2 = 10

TSET C1

5(K1 "Medium" "High")K2 K1 K1

END

DSET: Session command for making patterned data

DSET C

Creates a new column of dates and/or times that follow a pattern.

For example, the commands store the dates 1/1/15, 1/2/15, ..., 1/31/15 into C1.

DSET C1;

DSTART "1/1/15";

DEND "1/31/15";

DAY 1.

Note Minitab's default date/time formats can change depending on the Regional Settings of your device. For more information, go

to Default date/time formats on page 1140.

LIST

Date/time K...K or "text" ..."text".

FORMAT K

The format statement can be used for two purposes. It provides the format for the resulting patterned data.

If no format is given then the resulting column will be in the format of the last date/time value given either

on the DSTART or DEND subcommand lines. Also, it can be utilized as a user-defined format for specifying

the input format of the start and end date/time value. If no format statement is given, the date/time values

on the DSTART and DEND subcommands must be in a default format. For example:

DSET C1;

DSTART "1/1/15";

DEND "1/31/15";

FORMAT (DTm-d-yy);

DAY 1.

The format statement must begin with the characters DT followed by the desired date/time format. The

preceding example displays dates in the form 1-1-15.

Note Minitab's default date/time formats can change depending on the Regional Settings of your device. For more information,

go to Default date/time formats on page 1140.

DSTART K

DSTART specifies the beginning date/time value. The argument can be either a stored text constant or a text

string in double quotation marks. If you want to use a non-default date/time format for the text given with

DSTART and DEND, you must use the FORMAT subcommand. (If you are using a default date/time format,

FORMAT is not necessary.)

Note Minitab's default date/time formats can change depending on the Regional Settings of your device. For more information,

go to Default date/time formats on page 1140.

Minitab Statistical Software Manipulating and Calculating Data

DEND K

DEND specifies the ending date/time value. The argument can be either a stored text constant or a text string

in double quotation marks. If you want to use a non-default date/time format for the text given with DSTART

and DEND, you must use the FORMAT subcommand. (If you are using a default date/time format, FORMAT

is not necessary.)

Note Minitab's default date/time formats can change depending on the Regional Settings of your device. For more information,

go to Default date/time formats on page 1140.

RLIST K

RLIST specifies the number of times to repeat the list. K must be a positive integer greater than or equal to

RVALUE K

RVALUE specifies the number of times to repeat each value. K must be a positive integer greater than or

equal to 1.

Subcommands for incrementing the date/time data

You must use one (and only one) of the following subcommands with DSET to indicate how to increment the date/time

data. K must be a positive integer greater than or equal to 1.

The patterned data ends when a date/time value is greater than or equal to the end date/time value (DEND). The end

date/time value is part of the column only if the pattern equals the end date/time value.

Note Minitab's default date/time formats can change depending on the Regional Settings of your device. For more information, go to

Default date/time formats on page 1140.

DAY

Increments day by K units.

WDAY

Increments workday (M–F) by K units.

WEEK

Increments week by K units.

MONTH

Increments month by K units.

QUARTER

Increments quarter by K units.

YEAR

Increments year by K units.

HOUR

Increments hour by K units.

MINUTE

Increments minute by K units.

SECOND

Increment second by K units.

Minitab Statistical Software Manipulating and Calculating Data

HUNDREDTH

Increment hundredth by K units.

THOUSANDTHS

Increment thousandths by K units.

Make Mesh Data

MESH: Session command for making mesh data

MESH C C

Creates a mesh of regular x-y data and stores the data in C and C. (Data with a regular shape form a grid with

evenly spaced intervals. Data with an irregular shape are not located on evenly spaced intervals.)

You can use the mesh data for drawing surface and wireframe plots, with an option to create the z-data at the

same time.

XMESH K K K (optional)

Specifies the lowest x-value, the highest x-value, and the number of x-values. The default is – 5 5 11.

YMESH K K K (optional)

Specifies the lowest y-value, the highest y-value, and the number of y-values. The default is – 5 5 11.

%USERFUNC K C C C (optional)

Specifies your own function as indicated by function number K in the macro %USERFUNC. For instructions

to add your own function, go to Add your own function on page 1131.

PARAMS K...K (optional)

Specifies parameters for the functions. See the table below for formulas and the number of parameters.

Functions for z-values (optional)

The function subcommands in the following table generate Z-values for the mesh and store them in C.

Parameters (in order)EquationSubcommand

= MU1, σ

= S1, μ

= MU2, σ

= S2, ρ = RHO

(σ

≠ 0, σ

≠ 0, −1 < ρ < 1)

BVNORMAL C C

The following variables are derived from the

above variables, as follows:

A, B, CBOWL C

(A ≠ 0, B ≠ 0, C ≠ 0)

Minitab Statistical Software Manipulating and Calculating Data

Parameters (in order)EquationSubcommand

A, B, CCONE C

(A ≠ 0, B ≠ 0, C ≠ 0)

A, B, CCOWBOYHAT C

(A ≠ 0, B ≠ 0, C ≠ 0)

A, B, CEGGCARTON C

(A ≠ 0, B ≠ 0, C ≠ 0)

A, B, C, R

where if z < 0, set z = 0

HEMISPHERE C

(A ≠ 0, B ≠ 0, C ≠ 0, R ≠ 0)

A, B, CHILLANDDALE C

(A ≠ 0, B ≠ 0, C ≠ 0)

A, B, CSADDLE C

(A ≠ 0, B ≠ 0, C ≠ 0)

A, B, CWAVE C

(A ≠ 0, B ≠ 0, C ≠ 0)

Make Indicator Variables

INDICATOR: Session command for creating indicator variables

INDICATOR C C...C

Creates indicator variables (also called dummy variables) that you can use in a regression analysis. If you use

REGRESS on page 169, you do not need to create indicator variables.

The smallest number in C2 is 2 and the largest is 6. INDICATOR creates one indicator variable for each unique

value.

•

C11 is the indicator variable for the value 2. C11 contains a 1 in every row where C2 contains a 2, and 0

otherwise.

•

C12 is the indicator variable for the value 3. C12 contains a 1 in every row where C2 contains a 3, and 0

otherwise.

•

C13 is the indicator variable for 5. C13 contains a 1 in every row where C2 contains 5, and 0 otherwise.

•

C14 is the indicator variable for 6. C13 contains a 1 in every row where C2 contains 6, and 0 otherwise.

If C2 contains an * (missing data code), then all indicator variables are also set to *.

The number of storage columns must be equal to the number of distinct values (not including *) in the input

column. Up to 100 storage columns are allowed on INDICATOR.

Minitab Statistical Software Manipulating and Calculating Data

The following command language is an example of using INDICATOR.

INDICATOR C2, C11-C14

Before using INDICATOR

After using INDICATOR

C14C13C12C11...C1

0001...2

0100...5

0010...3

1000...6

0100...5

****...*

Random Data

BASE: Session command for fixing a starting number for the random number generator

BASE K

Fixes a starting point for Minitab's random number generator.

Minitab has a long string of random numbers available. Minitab normally chooses its own starting point for this

process. If Minitab always started at the beginning of the list, you would always get the same data. To avoid this,

Minitab uses the time of day to choose a random starting point in the string.

However, you might want to control where Minitab starts the string. For example, you may wish to repeat a

sequence by generating the same set of random data. In this case, the BASE command tells the random number

generator where to start. The generator will use this base until you set a new BASE or exit Minitab.

Note If you use the same base on different platforms or different versions of Minitab, you might not get the same random number

sequence.

Minitab Statistical Software Manipulating and Calculating Data

RANDOM: Session command for generating random data

RANDOM E [E]

Use RANDOM to generate and store a random sample of one or more observations from a specified distribution.

The subcommand specifies the distribution. If no subcommand is given, data are simulated from a normal

distribution with mu = 0 and sigma = 1.

Use the BASE on page 86 command to generate the same set of data more than once.

Suppose you want 50 random samples, each containing 20 observations, from a binomial distribution with number

of trials n = 5, and probability of success p = 0.3. To place each sample in a separate column, type the following

commands.

RANDOM 20 C1-C50;

BINOMIAL 5 0.3.

CHISQUARE K

Specifies distribution to sample, with degrees of freedom = K.

NORMAL [K [K]]

Specifies distribution to sample. Generates data from a standard normal. Optionally, specify mean = K, and

standard deviation = K.

MNORMAL C M

Specifies distribution to sample, with mean column = C, and variance-covariance matrix = M.

F K K

Specifies distribution to sample, with numerator degrees of freedom = K, denominator degrees of freedom

= K.

T K

Specifies distribution to sample, with degrees of freedom = K.

UNIFORM [K K]

Specifies distribution to sample. Generates data using lower endpoint = 0.0 and upper endpoint = 1.0.

Optionally, specify lower endpoint = K and upper endpoint = K.

BERNOULLI K

Specifies distribution to sample, with probability of success = K.

BINOMIAL K K

Specifies distribution to sample, with number of trials = K and event probability = K.

GEOMETRIC K

Specifies distribution to sample, with event probability = K.

NONEVENT

Models the number of nonevents before the first event occurs.

TOTAL

Models the total number of trials needed to produce one event.

NEGBINOMIAL K K

Specifies distribution to sample, with event probability = K and number of events needed = K.

Minitab Statistical Software Manipulating and Calculating Data

NONEVENT

Models the number of nonevents before the specified number of events occurs.

TOTAL

Models the total number of trials needed to produce the specified number of events.

HYPERGEOMETRIC K K K

Specifies distribution to sample, with population size = K, event count in population = K, and sample size =

DISCRETE C C

Specifies distribution to sample, with values in C and probabilities in C.

INTEGER K K

Specifies distribution to sample, with discrete uniform on integers from minimum value = K to maximum

value = K.

POISSON K

Specifies distribution to sample, with mean = K.

BETA K K

Specifies distribution to sample, with first shape parameter = K and second shape parameter = K.

CAUCHY [K [K]]

Specifies distribution to sample. Generates data using location = 0.0 and scale = 1.0. Optionally, specify

location = K and scale = K.

EXPONENTIAL [K [K]]

Specifies distribution to sample. Generates data using mean = 1.0 and threshold = 0.0. Optionally, specify

mean = K and threshold = K.

GAMMA K K [K]

Specifies distribution to sample, with shape = K, scale = K, and optionally, threshold = K.

LAPLACE [K [K]]

Specifies distribution to sample. Generates data using location = 0.0 and scale = 1.0. Optionally, specify

location = K and scale = K.

LEXTREME [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

LOGISTIC [K [K]]

Specifies distribution to sample. Generates data using location = 0.0 and scale = 1.0. Optionally, specify

location = K and scale = K.

LLOGISTIC [K [K [K]]]

Specifies distribution to sample. Generates data using location = 0.0, scale = 1.0, and threshold = 0.0.

Optionally, specify location = K, scale = K, and threshold = K.

LNORMAL [K [K [K]]]

Specifies distribution to sample. Generates data using location = 0.0, scale = 1.0, and threshold = 0.0.

Optionally, specify location = K, scale = K, and threshold = K.

Minitab Statistical Software Manipulating and Calculating Data

SEXTREME [K [K]]

Specifies distribution to sample. Generates data using location = 0.0 and scale = 1.0. Optionally, specify

location = K and scale = K.

TRIANGULAR K K K

Specifies distribution to sample, with lower endpoint = K, mode = K, and upper endpoint = K.

WEIBULL K K [K]

Specifies distribution sample, with shape = K, scale = K, and optionally, threshold = K.

SAMPLE: Session command for generating rows of random data from specified columns

SAMPLE K C...C

Generates K rows of random data from specified input columns, C...C, and stores in specified storage columns,

C...C.

Takes a random sample of rows. If you use REPLACE, you can select the same row more than once. If you use

NOREPLACE, you cannot select the same row more than once. If you do not use a subcommand, Minitab samples

without replacement.

Tip If there are K rows in C...C columns, you can randomize their order by sampling all K rows and storing in the original input columns.

For example, SAMPLE K C1 C1.

REPLACE

When you sample with replacement, a selected observation goes back into the pool of possible choices and

can be selected again.

NOREPLACE

When you sample without replacement, a selected observation can be chosen only once.

Probability Distributions

PDF: Session command for calculating the probability distribution of a continuous random

variable

PDF E [E]

Calculates density values or probabilities for the specified values in E from a standard normal distribution or

another specified distribution and stores in E.

•

For a discrete distribution, the probability distribution function (pdf) calculates probabilities for the specified

values (sometimes called the discrete probability distribution function). If you specify a discrete distribution

(BINOMIAL, GEOMETRIC, NEGBINOMIAL, HYPERGEOMETRIC, DISCRETE, INTEGER, POISSON), the arguments

on the PDF line are optional. If you do not specify arguments, Minitab displays a table of the distribution. If

you execute PDF from the menu, you must supply the input columns.

•

For a continuous distribution, pdf calculates the continuous probability density function (often called the

density function).

•

If you do not specify a distribution, results are generated for a normal distribution with mu = 0 and sigma =

Storage is optional. If you specify a storage column, pdf values are stored there and are not displayed. If you do

not specify a storage column, Minitab displays pdf values.

Minitab Statistical Software Manipulating and Calculating Data

CHISQUARE K [K]

Specifies distribution with degrees of freedom = K.

NORMAL [K [K]]

Specifies distribution. Generates data from a standard normal; optionally specify mean = K, and standard

deviation = K.

F K K

Specifies distribution, with numerator degrees of freedom = K, denominator degrees of freedom = K.

T K

Specifies distribution, with degrees of freedom = K.

UNIFORM [K K]

Specifies distribution. Generates data using lower endpoint = 0.0 and upper endpoint = 1.0. Optionally,

specify lower endpoint = K and upper endpoint = K.

BINOMIAL K K

Specifies distribution, with number of trials = K and event probability = K.

GEOMETRIC K

Specifies distribution, with event probability = K.

NONEVENT

Models the number of nonevents before the first event occurs.

TOTAL

Models the total number of trials needed to produce one event.

NEGBINOMIAL K K

Specifies distribution, with event probability = K and number of events needed = K.

NONEVENT

Models the number of nonevents before the specified number of events occurs.

TOTAL

Models the total number of trials needed to produce the specified number of events.

HYPERGEOMETRIC K K K

Specifies distribution, with population size = K, event count in population = K, and sample size = K.

DISCRETE C C

Specifies distribution, with values in C and probabilities in C.

INTEGER K K

Specifies distribution, with discrete uniform on integers from minimum value = K to maximum value = K.

POISSON K

Specifies distribution, with mean = K.

BETA K K

Specifies distribution, with first shape parameter = K and second shape parameter = K.

Minitab Statistical Software Manipulating and Calculating Data

CAUCHY [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

EXPONENTIAL [K [K]]

Specifies distribution. Generates data using mean = 1.0 and threshold = 0.0. Optionally, specify mean = K

and threshold = K.

GAMMA K K [K]

Specifies distribution, with shape = K, scale = K, and optionally, threshold = K.

LAPLACE [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

LEXTREME [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

LOGISTIC [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

LLOGISTIC [K [K [K]]]

Specifies distribution. Generates data using location = 0.0, scale = 1.0, and threshold = 0.0. Optionally, specify

location = K, scale = K, and threshold = K.

LNORMAL [K [K [K]]]

Specifies distribution. Generates data using location = 0.0, scale = 1.0, and threshold = 0.0. Optionally, specify

location = K, scale = K, and threshold = K.

SEXTREME [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

TRIANGULAR K K K

Specifies distribution, with lower endpoint = K, mode = K, and upper endpoint = K.

WEIBULL K K [K]

Specifies distribution, with shape = K, scale = K, and optionally, threshold = K.

CDF: Session command for calculating the cumulative probability of an x-value

CDF E [E]

Calculates probabilities for the specified values in E from a standard normal distribution or another specified

distribution and stores the probabilities in E.

The cumulative distribution function, cdf, for any value x is the probability that a random variable with the specified

distribution has a value less than or equal to x. That is:

CDF (x) = Pr (X < x)

Minitab Statistical Software Manipulating and Calculating Data

•

If you specify a discrete distribution (BINOMIAL, GEOMETRIC, NEGBINOMIAL, HYPERGEOMETRIC, DISCRETE,

INTEGER, POISSON), then the arguments on the CDF line are optional. If you do not specify an argument, then

Minitab displays a table of the distribution. (Entries where the cdf is less than 0.00005 or greater than 0.99995

might not be displayed in this table.) If you execute CDF from the menu, you must supply the input columns.

•

If you do not specify a distribution, then the results are generated for a normal distribution with mu = 0 and

sigma = 1.

Storage is optional. If you specify a storage column, the cdf values are stored there and are not displayed. If you

do not specify a storage column, Minitab displays the cdf values.

CHISQUARE K [K]

Specifies distribution with degrees of freedom = K, and, optionally, noncentrality parameter = K.

NORMAL [K [K]]

Specifies distribution. Generates data from a standard normal. Optionally, specify mean = K and standard

deviation = K.

F K K [K]

Specifies distribution, with numerator degrees of freedom = K, denominator degrees of freedom = K, and,

optionally, noncentrality parameter = K.

T K [K]

Specifies distribution, with degrees of freedom = K and, optionally, noncentrality parameter = K.

UNIFORM [K K]

Specifies distribution. Generates data using lower endpoint = 0.0 and upper endpoint = 1.0. Optionally,

specify lower endpoint = K and upper endpoint = K.

BINOMIAL K K

Specifies distribution, with number of trials = K and event probability = K.

GEOMETRIC K

Specifies distribution, with event probability = K.

NONEVENT

Models the number of nonevents before the first event occurs.

TOTAL

Models the total number of trials that are needed to produce one event.

NEGBINOMIAL K K

Specifies distribution, with event probability = K and number of events that are needed = K.

NONEVENT

Models the number of nonevents before the specified number of events occurs.

TOTAL

Models the total number of trials that are needed to produce the specified number of events.

HYPERGEOMETRIC K K K

Specifies distribution, with population size = K, event count in population = K, and sample size = K.

DISCRETE C C

Specifies distribution, with values in C and probabilities in C.

Minitab Statistical Software Manipulating and Calculating Data

INTEGER K K

Specifies distribution, with discrete uniform on integers from minimum value = K to maximum value = K.

POISSON K

Specifies distribution, with mean = K.

BETA K K

Specifies distribution, with first shape parameter = K and second shape parameter = K.

CAUCHY [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

EXPONENTIAL [K [K]]

Specifies distribution. Generates data using mean = 1.0 and threshold = 0.0. Optionally, specify mean = K

and threshold = K.

GAMMA K K [K]

Specifies distribution, with shape = K, scale = K, and optionally, threshold = K.

LAPLACE [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

LEXTREME [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

LOGISTIC [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

LLOGISTIC [K [K [K]]]

Specifies distribution. Generates data using location = 0.0, scale = 1.0, and threshold = 0.0. Optionally, specify

location = K, scale = K, and threshold = K.

LNORMAL [K [K [K]]]

Specifies distribution. Generates data using location = 0.0, scale = 1.0, and threshold = 0.0. Optionally, specify

location = K, scale = K, and threshold = K.

SEXTREME [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

TRIANGULAR K K K

Specifies distribution, with lower endpoint = K, mode = K, and upper endpoint = K.

WEIBULL K K [K]

Specifies distribution, with shape = K, scale = K, and optionally, threshold = K.

Minitab Statistical Software Manipulating and Calculating Data

INVCDF: Session command for calculating the variable for a cumulative probability

INVCDF E [E]

Calculates the inverse of the cdf for the specified values in E from a standard normal distribution or another

specified distribution and stores in E.

INVCDF returns the inverse of the cumulative distribution function (cdf), meaning that for a given probability, p,

INVCDF finds a value of x such that p = CDF(x).

For discrete distributions, INVCDF (p) = min {x such that CDF(x) > p}.

If you do not specify a distribution, results are generated for a normal distribution with mu = 0 and sigma = 1.

Storage is optional. If you specify a storage column, the INVCDF values are stored there and are not displayed. If

you do not specify a storage column, Minitab displays the INVCDF values.

CHISQUARE K [K]

Specifies distribution with degrees of freedom = K, and, optionally, noncentrality parameter = K.

NORMAL [K [K]]

Specifies distribution. Generates data from a standard normal distribution. Optionally, specify mean = K, and

standard deviation = K.

F K K [K]

Specifies distribution, with numerator degrees of freedom = K, denominator degrees of freedom = K, and,

optionally, noncentrality parameter = K.

T K [K]

Specifies distribution, with degrees of freedom = K and, optionally, noncentrality parameter = K.

UNIFORM [K K]

Specifies distribution. Generates data using lower endpoint = 0.0 and upper endpoint = 1.0. Optionally,

specify lower endpoint = K and upper endpoint = K.

BINOMIAL K K

Specifies distribution, with number of trials = K and event probability = K.

GEOMETRIC K

Specifies distribution, with event probability = K.

NONEVENT

Models the number of nonevents before the first event occurs.

TOTAL

Models the total number of trials that are needed to produce one event.

NEGBINOMIAL K K

Specifies distribution, with event probability = K and number of events needed = K.

NONEVENT

Models the number of nonevents before the specified number of events occurs.

TOTAL

Models the total number of trials that are needed to produce the specified number of events.

Minitab Statistical Software Manipulating and Calculating Data

HYPERGEOMETRIC K K K

Specifies distribution, with population size = K, event count in population = K, and sample size = K.

DISCRETE C C

Specifies distribution, with values in C and probabilities in C.

INTEGER K K

Specifies distribution, with discrete uniform on integers from minimum value = K to maximum value = K.

POISSON K

Specifies distribution, with mean = K.

BETA K K

Specifies distribution, with first shape parameter = K and second shape parameter = K.

CAUCHY [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

EXPONENTIAL [K [K]]

Specifies distribution. Generates data using mean = 1.0 and threshold = 0.0. Optionally, specify mean = K

and threshold = K.

GAMMA K K [K]

Specifies distribution, with shape = K, scale = K, and, optionally, threshold = K.

LAPLACE [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

LEXTREME [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

LOGISTIC [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

LLOGISTIC [K [K [K]]]

Specifies distribution. Generates data using location = 0.0, scale = 1.0, and threshold = 0.0. Optionally, specify

location = K, scale = K, and threshold = K.

LNORMAL [K [K [K]]]

Specifies distribution. Generates data using location = 0.0, scale = 1.0, and threshold = 0.0. Optionally, specify

location = K, scale = K, and threshold = K.

SEXTREME [K [K]]

Specifies distribution. Generates data using location = 0.0 and scale = 1.0. Optionally, specify location = K

and scale = K.

TRIANGULAR K K K

Specifies distribution, with lower endpoint = K, mode = K, and upper endpoint = K.

WEIBULL K K [K]

Specifies distribution, with shape = K, scale = K, and optionally, threshold = K.

Minitab Statistical Software Manipulating and Calculating Data

Resampling Analyses

BTFT: Session command for calculating a 1-sample bootstrap confidence interval of a function

BTFT C K

BTFT calculates a confidence interval for a function in a population. The C specifies the column that contains a

sample from the population. The K specifies the number of resamples. The number of resamples can be from 1

to 10,000. The available functions are mean, median, sum, variance, and standard deviation.

Use the appropriate subcommand to choose the function. Specify only one of MEAN, MEDIAN, SUM, VARIANCE,

and STDEV.

MEAN

Calculates a confidence interval for the mean.

MEDIAN

Calculates a confidence interval for the median.

SUM

Calculates a confidence interval for the sum.

VARIANCE

Calculates a confidence interval for the variance.

STDEV

Calculates a confidence interval for the standard deviation.

Options

ITYPE K

Enter K to specify the type of confidence interval.

Format of the alternative hypothesisValue of K

Lower bound–1

Two-sided0 (default)

Upper bound1

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

BASE K

Fixes a starting point for Minitab's random number generator.

Minitab Statistical Software Manipulating and Calculating Data

Normally, Minitab uses the time of day to start the random number generator. Instead, you can use the BASE

subcommand to specify a number to use to start the generator. Use the BASE subcommand so that you can

generate the same sample repeatedly.

Note If you use the same base on different platforms or different versions of Minitab, you might not get the same random number

sequence.

SSAMPLE C

Stores the statistics from the resampling process.

Results

Use the following subcommands to control what output Minitab produces.

TSTATISTICS

Displays summary statistics for the sample: mean, standard deviation, variance, sum, minimum, median, and

maximum.

TBOOTSTRAP

Displays the confidence interval and the associated statistics.

GHISTOGRAM

Displays a histogram of the resampled statistics. GHISTOGRAM does not work if the number of resamples is 1 or

if you specify GINDIVIDUAL.

GINDIVIDUAL

Displays an individual value plot that compares the resample to the original sample. GINDIVIDUAL does not work

if the number of resamples is greater than 1 or if you specify GHISTOGRAM.

BTPR: Session command for calculating a 1-sample bootstrap confidence interval of a

proportion

BTPR C K

BTPR K K K

BTPR calculates a confidence interval for a proportion in a population. In the first parameterization, the C specifies

the column that contains a sample from the population. The column must contain 1 or 2 unique, non-missing

values. The K specifies the number of resamples. The number of resamples can be from 1 to 10,000.

In the second parameterization, the first K represents the number of events. The first K must be greater than or

equal to 1. The second K represents the number of trials. The number of trials must be greater than the number

of events and less than 100,000. The third K is the number of resamples. The number of resamples must be from

1 to 10,000.

EVENT K

When the sample data are in a column, use EVENT to specify the value in the column that the proportion should

be for. The value of K matches one of the values in the sample column. Enclose text values in quotation marks.

For example, to calculate an interval for the proportion of a "Fail" in the sample column, enter EVENT "Fail".

Minitab Statistical Software Manipulating and Calculating Data

Options

ITYPE K

Enter K to specify the type of confidence interval.

Format of the alternative hypothesisValue of K

Lower bound–1

Two-sided0 (default)

Upper bound1

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

BASE K

Fixes a starting point for Minitab's random number generator.

Normally, Minitab uses the time of day to start the random number generator. Instead, you can use the BASE

subcommand to specify a number to use to start the generator. Use the BASE subcommand so that you can

generate the same sample repeatedly.

Note If you use the same base on different platforms or different versions of Minitab, you might not get the same random number

sequence.

SSAMPLE C

Stores the proportions from the resampling process.

Results

Use the following subcommands to control what output Minitab produces.

TSTATISTICS

Displays summary statistics for the sample: sample size and proportion.

TBOOTSTRAP

Displays the confidence interval and the associated statistics.

GHISTOGRAM

Displays a histogram of the resampled proportions. GHISTOGRAM does not work if the number of resamples is

1 or if you specify GINDIVIDUAL.

GBARCHART

Displays a bar chart that compares the resample to the original sample. GBARCHART does not work if the number

of resamples is greater than 1 or if you specify GHISTOGRAM.

Minitab Statistical Software Manipulating and Calculating Data

BTTM: Session command for calculating a 2-sample bootstrap confidence interval for the

difference of means

BTTM C C K

BTTM calculates a confidence interval for the difference between the means of two populations. The columns can

have one of two formats:

•

The first column contains the measurements. The second column identifies which population each observation

is from.

•

Each column contains a sample from a different population. Use UNSTACKED to specify that the data are in

this format.

The value of K is the number of resamples. The number of resamples must be from 1 to 10,000.

The difference is the first sample minus the second sample. When the data are stacked, the first sample depends

on the values in the identification column. The first value is the lowest number, the first value alphabetically, the

earliest date, or the earliest value in a previously-specified value order. When the data are unstacked, the first

sample is in the first C after BTTM.

UNSTACKED

Indicates that the columns after BTTM contain different samples.

Options

ITYPE K

Enter K to specify the type of confidence interval.

Format of the alternative hypothesisValue of K

Lower bound–1

Two-sided0 (default)

Upper bound1

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

BASE K

Fixes a starting point for Minitab's random number generator.

Normally, Minitab uses the time of day to start the random number generator. Instead, you can use the BASE

subcommand to specify a number to use to start the generator. Use the BASE subcommand so that you can

generate the same sample repeatedly.

Note If you use the same base on different platforms or different versions of Minitab, you might not get the same random number

sequence.

SDIFFERENCES C

Stores the differences from the resampling process.

Minitab Statistical Software Manipulating and Calculating Data

Results

Use the following subcommands to control what output Minitab produces.

TOBSERVED

Displays summary statistics for the samples: mean, standard deviation, variance, sum, minimum, median, and

maximum.

TDIFFERENCES

Displays the difference between the sample means.

TBOOTSTRAP

Displays the confidence interval and the associated statistics.

GHISTOGRAM

Displays a histogram of the resampled differences. GHISTOGRAM does not work if the number of resamples is 1

or if you specify GINDIVIDUAL.

GINDIVIDUAL

Displays an individual value plot that compares the resamples to the original samples. GINDIVIDUAL does not

work if the number of resamples is greater than 1 or if you specify GHISTOGRAM.

RNMN: Session command for performing a 1-sample randomization test of a mean

RNMN C K K

RNMN performs a hypothesis test that the mean of the population equals the second K. The C specifies the column

that contains a sample from the population. The first K specifies the number of resamples. The number of resamples

can be from 1 to 10,000.

Options

ALTERNATIVE K

Enter K to specify the direction of the alternative hypothesis.

Format of the alternative hypothesisValue of K

Less than–1

Not equal to0 (default)

Greater than1

BASE K

Fixes a starting point for Minitab's random number generator.

100

Minitab Statistical Software Manipulating and Calculating Data

Normally, Minitab uses the time of day to start the random number generator. Instead, you can use the BASE

subcommand to specify a number to use to start the generator. Use the BASE subcommand so that you can

generate the same sample repeatedly.

Note If you use the same base on different platforms or different versions of Minitab, you might not get the same random number

sequence.

SMEAN C

Stores the means from the resampling process.

Results

Use the following subcommands to control what output Minitab produces.

TOBSERVED

Displays summary statistics for the sample: mean, standard deviation, variance, sum, minimum, median, and

maximum.

TRANDOMIZATION

Displays the results of the hypothesis test.

GHISTOGRAM

Displays a histogram of the resampled means. GHISTOGRAM does not work if the number of resamples is 1 or if

you specify GINDIVIDUAL.

GINDIVIDUAL

Displays an individual value plot that compares the resample to the original sample. GINDIVIDUAL does not work

if the number of resamples is greater than 1 or if you specify GHISTOGRAM.

RNPR: Session command for performing a 1-sample randomization test of a proportion

RNPR C K K

RNPR K K K K

RNPR performs a hypothesis test on a proportion in a population. In the first parameterization, the C specifies

the column that contains a sample from the population. The column must contain 1 or 2 unique, non-missing

values. The first K specifies the number of resamples. The number of resamples can be from 1 to 10,000. The last

K is the hypothesized proportion. The hypothesized proportion must be a value between 0 and 1.

In the second parameterization, the first K represents the number of events. The first K must be greater than or

equal to 1. The second K represents the number of trials. The number of trials must be greater than the number

of events and less than 1 million. The third K is the number of resamples. The number of resamples must be from

1 to 10,000. The last K is the hypothesized proportion. The hypothesized proportion must be a value between 0

and 1.

Options

ALTERNATIVE K

Enter K to specify the direction of the alternative hypothesis.

101

Minitab Statistical Software Manipulating and Calculating Data

Format of the alternative hypothesisValue of K

Less than–1

Not equal to0 (default)

Greater than1

BASE K

Fixes a starting point for Minitab's random number generator.

Normally, Minitab uses the time of day to start the random number generator. Instead, you can use the BASE

subcommand to specify a number to use to start the generator. Use the BASE subcommand so that you can

generate the same sample repeatedly.

Note If you use the same base on different platforms or different versions of Minitab, you might not get the same random number

sequence.

SPROPORTION C

Stores the proportions from the resampling process.

Results

Use the following subcommands to control what output Minitab produces.

TOBSERVED

Displays summary statistics for the sample: sample size and proportion.

TRANDOMIZATION

Displays the results of the hypothesis test.

GHISTOGRAM

Displays a histogram of the resampled proportions. GHISTOGRAM does not work if the number of resamples is

1 or if you specify GINDIVIDUAL.

GBARCHART

Displays a bar chart that compares the resample to the original sample. GBARCHART does not work if the number

of resamples is greater than 1 or if you specify GHISTOGRAM.

RNTM: Session command for performing a 2-sample randomization test of means

RNTM C C K

RNTM performs a hypothesis test that the means of two populations are equal. The columns can have one of two

formats:

•

The first column contains the measurements. The second column identifies which population each observation

is from.

•

Each column contains a sample from a different population. Use UNSTACKED to specify that the data are in

this format.

The value of K specifies the number of resamples. The number of resamples must be from 1 to 10,000.

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Minitab Statistical Software Manipulating and Calculating Data

The difference is the first sample minus the second sample. When the data are stacked, the first sample depends

on the values in the identification column. The first value is the lowest number, the first value alphabetically, the

earliest date, or the earliest value in a previously-specified value order. When the data are unstacked, the first

sample is in the first C after RNTM.

UNSTACKED

Indicates that the columns after RNTM contain different samples.

Options

ALTERNATIVE K

Enter K to specify the direction of the alternative hypothesis.

Format of the alternative hypothesisValue of K

The mean of the first sample is less than the mean of the second sample.–1

The mean of the first sample is not equal to the mean of the second sample.0 (default)

The mean of the first sample is greater than the mean of the second sample.1

BASE K

Fixes a starting point for Minitab's random number generator.

Normally, Minitab uses the time of day to start the random number generator. Instead, you can use the BASE

subcommand to specify a number to use to start the generator. Use the BASE subcommand so that you can

generate the same sample repeatedly.

Note If you use the same base on different platforms or different versions of Minitab, you might not get the same random number

sequence.

SDIFFERENCES C

Stores the differences from the resampling process.

Results

Use the following subcommands to control what output Minitab produces.

TOBSERVED

Displays summary statistics for the samples: mean, standard deviation, variance, sum, minimum, median, and

maximum.

TRANDOMIZATION

Displays the results of the hypothesis test.

TDIFFERENCES

Displays the difference between the sample means.

GHISTOGRAM

Displays a histogram of the resampled differences. GHISTOGRAM does not work if the number of resamples is 1

or if you specify GINDIVIDUAL.

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Minitab Statistical Software Manipulating and Calculating Data

GINDIVIDUAL

Displays an individual value plot that compares the resamples to the original samples. GINDIVIDUAL does not

work if the number of resamples is greater than 1 or if you specify GHISTOGRAM.

Matrices

READ data into a matrix

READ K K M

Puts numbers into a matrix. To input data to columns, go to READ data into columns on page 41. You can specify

the filename as either the name of the file in double quotes, or a stored text constant. If the file has an extension

other than DAT and/or if it is not in your current directory, include the file name extension and the path within

quotation marks.

You can use either spaces or commas to separate the data in the matrix.

You must specify the dimension of the matrix in the READ command. The first K gives the number of rows, the

second K the number of columns. The M is the matrix identifier for storage. If a file name is not used, READ is

followed by data lines, each containing one row of the matrix. The following command creates the following

matrix.

Command

READ 3 4 M2

1 2 3 4

5 6 7 8

9 10 11 12

END

Matrix

1 2 3 4

5 6 7 8

9 10 11 12

FILE "filename"

FILE K

Reads or inserts data from the specified text file.

DEFINE: Session command for defining a constant matrix

DEFINE K K K M

Defines a constant matrix where K is the value, K is the number of rows, K is the number of columns, and M is the

stored matrix. For example, the following command language creates a matrix, M1, has 4 rows and 3 columns,

and all of whose entries are 1.

DEFINE 1 4 3 M1

DIAGONAL: Session command for creating a matrix from a column

DIAGONAL C M

Forms a matrix out of a column. If C has n entries, then M will be an n x n matrix with C as its diagonal and zeros

elsewhere.

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DIAGONAL M C

Takes the diagonal of a matrix and puts it into a column.

INVERT: Session command for replacing a matrix value with its inverse

INVERT M M

Replaces each value in the matrix with its inverse. The first M is the matrix to be replaced. The second M is the

new inverted matrix. The matrix must be square.

TRANSPOSE: Session command for changing rows to columns, and columns to rows

TRANSPOSE C...C

TRANSPOSE C M

TRANSPOSE M C

TRANSPOSE M [M]

TRANSPOSE reconfigures data so that rows become columns and columns become rows.

•

TRANSPOSE C...C transposes the values in the columns.

•

TRANSPOSE C M transposes the values in a column to a matrix.

•

TRANSPOSE MC transposes the values in a single row matrix to a column.

•

TRANSPOSE M [M] transposes the values in a matrix to another matrix.

VARNAMES C

Specifies a column that contains variable names for the transposed columns.

STORE C...C

STORE M

Specifies where the transposed data are to be written. You can store the data in columns or a matrix. When

you transpose data to columns, the number of columns specified must equal the number of rows in the

original columns.

If you do not use STORE, the data are stored in a new worksheet.

LABELS C

Specifies the column in which the labels of the transposed column or columns or matrix are stored.

NEWWS

NEWWS "text"

NEWWS K

Stores the results in a new worksheet. If you do not specify a worksheet name in "text" or in a constant (K)

argument, then Minitab uses the default naming of worksheets.

Important You cannot use NEWWS in a local macro. For more information, go to Session commands that are not allowed in

macros on page 1179.

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AFTER

Appends the transposed data into empty columns after the last column of data in the current worksheet.

Important You cannot use AFTER in a local macro. For more information, go to Session commands that are not allowed in

macros on page 1179.

EIGEN: Session command for calculating eigenvalues

EIGEN M C [M]

Calculates eigenvalues (also called characteristic values or latent roots) and eigenvectors for a symmetric matrix.

The eigenvalues are stored in decreasing order of magnitude down the column. The eigenvectors are stored as

columns of the matrix. The first column corresponds to the first eigenvalue (largest magnitude), the second column

to the second eigenvalue, and so on.

Data

INFO: Session command for summarizing the current worksheet

INFO [C...C]

Summarizes the current worksheet.

If no columns are specified, INFO prints a list of all columns used with their names and counts, all stored constants,

all matrices. If there are missing observations, a count of these is also given. If a column contains text data, the

letter T is printed to the left of the column. If columns have assigned formulas, these are printed along with the

method selected for updating the calculations (manual or automatic). If you list columns, information is given on

just those columns.

MERGE: Session command for merging two worksheets into one

worksheet

MERGE

Merges two worksheets into one new worksheet. You can combine any two open worksheets using MERGE. Unlike

the merging option available when opening additional worksheets, MERGE duplicates, and then combines, the

information from two original worksheets into a new worksheet. The default setting combines the worksheets,

side by side and with their existing attributes, into the new worksheet, named Merge Worksheet by default.

You can also customize the merged worksheet using the optional subcommands. You can use BY to combine the

worksheets according to the order and length of one or more columns. In addition, you can specify whether or

not to include unmatched values, missing values or multiple values from one or both of the BY columns. You can

also specify which columns will be included from each original worksheet using INCLUDE.

Note Stored constants, matrices, DOE objects, and worksheet descriptions do not transfer into the merged worksheet.

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NAME K

Specifies the name K for the new worksheet. The argument is a text constant, stated or stored. By default,

the name is Merge Worksheet.

WORKSHEET K

Specifies the name of one of the worksheets to be merged. The argument is a text constant, stated or stored.

This subcommand must be given twice.

BY C...C

Standardizes the combination of two worksheets. The order of the data within the BY columns becomes

ascending. The length and order of the remaining columns in the merged worksheet are manipulated

according to the BY column adjustments, no matter the order or length of the original columns.

The length of the columns also depends on how you would like to handle multiple, unmatched, and

missing observations within the BY columns. The adjusted length of the BY column or columns is still

applied to the order and length of the remaining columns.

Requirements for BY columns are as follows:

•

The column names for each of the two worksheets do not have to be unique. Minitab will create a

unique name for the columns in the merged worksheet based on the original worksheet names.

•

You must specify at least one pair of BY columns (one column from each worksheet) in the BY Columns

sub-dialog box. Additional BY columns must also come in pairs.

•

BY columns must have the same data type (numeric, date/time, or text).

•

When merging columns with value ordered text, the value orders in the BY columns must be the

same for both worksheets. For more information on value ordering, go to VORDER: Session command

for controlling the order for text categories to be processed by Minitab commands on page 137.

•

Column lengths must be the same between multiple BY columns.

•

Columns that are not the same lengths as the BY columns are excluded from the merged worksheet.

A note in the output documents the number of columns excluded.

NOMULTIPLES

Ignores all but the first row with the same values for the BY columns. If more than one row in a worksheet

has values for the BY columns, then only the first such row is used.

Including multiple observations indicates that you wish to keep the observations of the BY column that

are repeated within the column, regardless of the BY column values in the other worksheet. However,

the values can still be matched to multiple observations in the BY columns of the other worksheet,

depending on whether or not NOMULTIPLES is used for the other worksheet. Therefore, you could end

up with longer columns in the merged worksheet. For example, if there are two rows in one worksheet

and three rows in the other, all with the same values of the BY columns, then six rows would result.

NOMULTIPLES is available for each of the worksheets to be merged.

NOUNMATCHED

Removes unmatched BY column rows from the merged worksheet. If any row in a worksheet has values

for the BY columns that are not matched in the BY columns for the other worksheet, then that row is

not used.

By default, unmatched values are included which means that all of the rows in the opposite worksheet

that do not have matching BY column values are included. Missing value symbols would be added to

the cells for the entire row associated with the unmatched BY column values for the opposite worksheet.

Consequently, padding rows with missing values will make all of the columns for both of the worksheets

the same length as the BY columns.

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NOUNMATCHED is available for each of the worksheets to be merged.

INCLUDE C...C

Specifies which columns to include from the worksheet, whether you merge worksheets using the default

settings or using BY Columns. By default, all columns are included.

MISSINGS

Includes missing values within BY columns for both worksheets. The missing observations will be treated as

distinct values. The missing BY column value will be matched with a missing BY column value in the other

worksheet.

Note Missing values in text columns are represented by blanks and missing values in numeric and date/time columns are

represented by an asterisk.

SORT: Session command for sorting columns

SORT [C...C C...C]

Sorts one or more columns.

The default is to sort by the first column and carry along additional columns. Sorting by multiple columns is done

with the BY subcommand. SORT handles any combination of alpha or numeric columns.

The following is a simple example of the default method. Sorting is done based on the first column specified, C2.

The next two columns, C3 and C4, are carried along. Sorting is done in ascending order unless you use the

subcommand DESCENDING.

SORT C2 C3 C4 C12 C13 C14

Before sorting

C4C3C2

-1102

-1113

-3121

-1134

-1145

After sorting

C14C13C12

-3121

-1102

-1113

-1134

-1145

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BY C...C

Specifies the columns to use to sort the worksheet. Rows are first sorted by the first column listed following

BY, then, within that, by the second column, then, within that, by the third, and so on.

SORT C2 C3 C4 C12 C13 C14;

BY C2.

Before sorting with BY

C4C3C2

-1102

-1113

-3121

-1134

-1145

After sorting with BY

C14C13C12

-3121

-1102

-1113

-1134

-1145

DESCENDING C...C

Requests that sorting be done in descending, rather than the default ascending, order. Columns listed on

DESCENDING must also be listed on BY, or must be the first column listed on SORT if no BY is used.

SORT C3 C23;

BY C2;

DESCENDING C2.

Before sorting with DESCENDING

C3C2

102

113

121

134

145

After sorting with DESCENDING

C23

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C23

UNEQUAL

If the columns to be sorted have different numbers of rows, adds missing values so that the columns have an

equal number of rows.

Storage

Important You cannot use NEWWS and AFTER local macros. For more information, go to Session commands that are not allowed in

macros on page 1179.

Note These subcommands are optional, and are mutually exclusive. If you do not use one of these subcommands, results are stored in

the columns that you specify with the main command.

NEWWS

Stores the results in a new worksheet, with the default name.

NEWWS ["text"]

NEWWS [K]

Stores the results in a new worksheet. If you do not specify a worksheet name in a "text" argument or in a constant

(K) argument, then Minitab uses the default naming of worksheets.

AFTER ["text"]

Stores the results at the end of the specified worksheet. If you do not specify a worksheet name in a "text" argument

or in a constant (K) argument, then Minitab stores the copied data at the end of the active worksheet.

ORIGINAL

Stores the results in the original columns.

RANK: Session command for ranking values in a column

RANK

Calculates and stores the ranks of the input column. Assigns the numeral 1 to the smallest value, the numeral 2

to the second smallest value, the numeral 3 to the third smallest value, and so on. Ties are assigned the average

rank.

Note RANK works only with numeric columns.

The following command language ranks the values in C1 and puts the ranked values in C2.

RANK C1 C2

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Before ranking

0.5

1.0

1.5

1.0

2.0

0.0

After ranking

C2C1

2.00.5

3.51.0

5.01.5

3.51.0

6.02.0

1.00.0

DELETE: Session command for deleting rows of data

DELETE K...K C...C

Deletes rows K...K from columns C...C, and moves the remaining rows up to close the gap. DELETE works with both

text and numeric columns.

For example, the following command language changes the worksheet as shown below.

DELETE 2 5 6 C2-C4

Before DELETE

C4C3C2

154423

175531

143422

153326

167632

158630

155424

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After DELETE

C4C3C2

154423

143422

153326

155424

You can abbreviate a list of consecutive rows by using a colon. For example, to delete rows 1 through 10 and

rows 25 through 30 from C1, use the following command language.

DELETE 1:10 25:30 C1

SPLIT: Session command for splitting a worksheet into multiple

worksheets

SPLIT

Important SPLIT cannot be used in local macros. For more information, go to Session commands that are not allowed in macros on

page 1179.

The SPLIT command splits a worksheet into multiple new worksheets, one for each combination of a set of BY

variables. Each new worksheet is automatically named to reflect the combination of the BY variables.

Use SUBSET on page 114 to copy specified rows from the active worksheet to a new worksheet. With this command,

you can specify the subset based on row numbers, brushed points on a graph, or a condition such as unmarried

males under 50 years old.

BY C...C (required)

Specifies the columns to use to subset the worksheet. A new worksheet will be created for each unique value

or combination of values in the columns. Columns must be non-empty, all the same length, and can be of

any data type.

COLUMNS C...C

Specifies the columns to be copied to the new worksheet. Columns must be the same length as the BY

columns. Columns other than these are not copied.

MATRICES M...M

Specifies the matrices to be copied to the new worksheet. Matrices must has the same number of rows as

the BY columns. Matrices other than these are not copied.

NOMATRICES

Prevents matrices from being copied to the new worksheets.

NOCONSTANTS

Prevents stored constants from being copied to the new worksheets.

MISSINGS

Specifies that missing values are treated as a distinct value of the BY variable.

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DATE: Session command for changing data type to date/time

DATE C...C C...C

Changes data type of C...C to date/time and places results in C...C.

FORMAT

Indicates the format of the new date/time column or columns. For example:

•

FORMAT (DTm/d/yy) formats a new column as 5/25/16

•

FORMAT (DTm-d-yy) formats a new column as 5-25-16

For multiple columns, add the number of columns to the format statement. For example, for 2 columns, use

FORMAT (2DTm-d-yy).

For more information on date/time formats, go to Default date/time formats on page 1140.

ERASE: Session command for erasing variables

ERASE E...E

Erases any combination of columns (including their names), constants, and matrices.

Erasing all variables that you no longer need is a good practice.

ROWTOC: Session command for stacking multiple columns into

one column

ROWTOC C...C C

Stacks rows in C...C and places them in C. Enter the columns that contain the data you want to appear in a single

column, and then enter a column number or name in which to store the stacked data.

EXPAND C...C

Expands the specified column or columns, while stacking the rows.

EXSTORE C...C

Stores the expanded columns in the specified column.

SUBSCRIPTS C

Stores row subscripts in the specified column.

CSUBS C

Stores column subscripts in the specified column.

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SUBSET: Session command for copying specified rows to a new

worksheet

SUBSET

Important You cannot use SUBSET in a local macro. For more information, go to Session commands that are not allowed in macros

on page 1179.

Use SUBSET to copy specified rows from the active worksheet to a new worksheet. With this command, you can

specify the subset based on rows that match conditions, row numbers, formatted rows, brushed points on a graph,

or a formula that you specify, such as unmarried males under 50 years old.

One of INCLUDE and EXCLUDE must be used.

One of BRUSHED, WHERE, ROWS, conditions for values in a column, or conditions for row numbers, must be used.

If more than one is used, only the last valid one is honored.

Use SPLIT on page 112 to split, or unstack, the active worksheet into two or more new worksheets based on one

or more "By" variables. SUBSET and SPLIT always copy data to new worksheets.

INCLUDE

Includes the specified rows.

INCLUDE and EXCLUDE are mutually exclusive: the last one issued is honored. Choose INCLUDE or EXCLUDE

based on the degree of subsetting. For example, if you wish to use all but a few select rows in creating your

graph, it is more efficient to use EXCLUDE and name a small number of rows with the ROWS subsubcommand.

EXCLUDE

Excludes the specified rows.

INCLUDE and EXCLUDE are mutually exclusive: the last one issued is honored. Choose INCLUDE or EXCLUDE

based on the degree of subsetting. For example, if you wish to use all but a few select rows in creating your

graph, it is more efficient to use EXCLUDE and name a small number of rows with the ROWS subsubcommand.

MINCLUDE

Includes rows that contain missing values in the subset column.

MINCLUDE and MEXCLUDE are mutually exclusive: the last one issued is honored.

MEXCLUDE

Excludes rows that contain missing values in the subset column.

MINCLUDE and MEXCLUDE are mutually exclusive: the last one issued is honored.

AND

Allows you to specify multiple conditions. For example, LT C1 50 AND EQUAL C2 'Blue' denotes rows that

contain 50 or less in C1 and contain 'Blue' in C2.

Allows you to specify multiple conditions.

Conditions for values in a column

These subcommands specify values in a column. For example:

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•

ANY C1 22 23 27 specifies the rows in C1 that contain 22, 23, and 27.

•

NEQUALS C1 'male' specifies the rows in C! that do not contain 'male'.

ANY C K...K

Values in C that equal any of the specified values.

EQUALS C K

Values in C that equal the specified value.

NEQUALS C K

Values in C that do not equal the specified value.

Conditions for values in a numeric or date/time column

These subcommands specify values in a numeric or date/time column. For example:

•

GT C1 30 specifies the values in C1 that are greater than 30.

•

LTLT C1 30 70 specifies the values in C1 that are greater than 30 and less than 70.

LT C K

Values in C that are less than K.

LE C K

Values in C that are less than or equal to K.

GT C K

Values in C that are greater than K.

GE C K

Values in C that are greater than or equal to K.

LELE K C K

Values in C that are greater than or equal to K and less than or equal to K.

LELT K C K

Values in C that are greater than or equal to K and less than K.

LTLE K C K

Values in C that are greater than K and less than or equal to K.

LTLT K C K

Values in C that are greater than K and less than K.

Conditions for values in a text column

These commands specify values in a text column. The text value must be in single quotation marks. For example:

•

BEGINS C1 'm' specifies all values in C1 that begin with "m".

•

NCONTAINS C1 'non' specifies all values in C1 that contain "non".

BEGINS C K

Values in C that begin with K (text columns only).

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ENDS C K

Values in C that end with K (text columns only).

CONTAINS C K

Values in C that contain K (text columns only).

NCONTAINS C K

Values in C that do not contain K (text columns only).

Conditions for row numbers

ROWS K...K

Specifies row numbers K through K. List all row numbers to be included or excluded with a space between each.

Denote a patterned range by K:K/K. For example, 10:50/5 denotes all values from 10 to 50 by intervals of 5.

ROWLE K

The row number is less than or equal to K.

ROWGE K

The row number is greater than or equal to K. For example, ROWGE 12 denotes row numbers that are greater

than or equal to 12.

ROWBETWEEN K K

The row number is between K and K inclusive. The first value must be less than or equal to the second.

Other conditions

BRUSHED

Specifies the rows that are currently brushed.

WHERE expression

Specify a formula for subsetting. This formula must be in quotes. For example:

•

To subset where C1 is 2, enter WHERE "C1 = 2".

•

To subset where C2 = a, enter WHERE "C2 = ""a""".

•

To subset where C3 = 3/3/16, enter WHERE "C3 = DATE(""3/3/98"")".

CFORMAT C

Specifies a column that includes formatted rows.

Options for the new worksheet

NAME "text"

NAME K

Specifies the name of the new worksheet in a "text" argument or in a constant (K).

COLUMNS C...C

Specifies one or more columns of the appropriate length to be copied to the new worksheet. Columns other than

these are not copied.

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EQLN

Includes in the subset only columns that are the same length as the column with the condition. If this subcommand

is not used, all columns in the worksheet are included in the subset.

MATRICES M...M

Specifies one or more matrices with the appropriate number of rows to be copied to the new worksheet. Matrices

other than these are not copied.

NOMATRICES

Prevents matrices from being copied to the new worksheet.

NOCONSTANTS

Prevents stored constants from being copied to the new worksheet.

READ data into columns

READ C...C

Reads in data, row by row, that you type from the keyboard, or that you import from a text file. You cannot type

comments on a data line when the FORMAT subcommand is used.

READ enters new data into columns, replacing any data already in those columns, if it exists. For information on

entering data into a matrix, go to READ data into a matrix on page 43.

When you enter data manually, type END. after you enter your final value.

When you use READ, you can use a space or a comma to separate data entries. For example:

READ C1 C5.

1 2

3,4

END.

For details on using this command without subcommands to select data entry options, go to Using READ without

subcommands on page 1183.

FILE "filename"

Inserts data from the specified text file. You may specify the filename as either the name of the file in double

quotes, or a stored text constant. If the file has an extension other than DAT and/or if it is not in your current

directory, include the file extension and the path within the single quotation marks. For example, use the

following command to read a copy of the file SALES.ASC stored in the subdirectory JANUARY underneath

the directory SMITH on the C drive.

READ C1-C5;

FILE "C:\SMITH\JANUARY\SALES.ASC".

FORMAT (format statement)

Include a format statement, within parentheses to specify precisely how to enter data into the worksheet.

The entire expression within the parentheses is repeated once for each record. For more information, go to

Using READ with FORMAT on page 1183.

The FORMAT subcommand is useful when you want to skip over spaces, read data that have no spaces

between them, insert decimal points in numbers, or read in text data, date/time data, or currency data.

Format items may be combined together. For example, the following command reads the name in the first

20 spaces of each data line into Name (C12), skips the next 10 spaces (spaces 21 through 30), then reads the

number in space 31 into C1, the number in space 32 into C2, ..., the number in space 40 into C10.

NAME C12 'Name'

READ 'Name' C1-C10;

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FILE "MYDATA";

FORMAT(A20, 10X, 10F1).

Minitab has a special date/time (DT) format which works as shown below. This says to read the date/time

value in the first 8 spaces in the file into C1, and that the format of the date/time data in the file is m/d/yy.

READ C1;

FILE "DATEDATA";

FORMAT(DT8m/d/yy).

The following example shows the use of a decimal indicator, repeat factor in front of parentheses, and the

slash.

READ C11-C15;

FILE "EMPLOYEEDATA";

FORMAT (F2.1, 2(1X,F3), F4/F2).

This example uses two data lines for every row read. From the first line, the value of C11 is in spaces 1 and

2. The first value is a whole number and the second value is in the tenths place. The format skips space 3.

Then, C12 is read from spaces 4 to 6. The format skips space 7. Then, C13 is read from spaces 8 to 10, which

repeats the pattern inside of the parentheses. C14 is read from spaces 11 to 14. In response to the /, reading

moves to the second data line, and C15 is read from spaces 1 and 2. For more information, go to Valid format

items on page 1186.

NOBS K

The NOBS subcommand specifies the number of observations (rows) to be inserted. If an END on page 38

subcommand or end-of-file is encountered before K observations are inserted, NOBS is ignored. NOBS is

useful when you want to insert just the first portion of a file. It is also useful for Prompting a user for

information on page 1177.

SKIP K

Tells Minitab to skip K lines at the top of the data file before beginning to add data into the file. This is most

useful when you have one or more lines of text, such as column names and titles, at the top of a data file

that you want to import into Minitab.

With READ; FILE only

DECIMAL works with READ when only when reading a file. DECIMAL does not work with READ if you type data.

DECIMAL ","

DECIMAL "."

Specifies a comma or period as a decimal separator.

READ data into a matrix

READ K K M

Puts numbers into a matrix. To input data to columns, go to READ data into columns on page 41. You can specify

the filename as either the name of the file in double quotes, or a stored text constant. If the file has an extension

other than DAT and/or if it is not in your current directory, include the file name extension and the path within

quotation marks.

You can use either spaces or commas to separate the data in the matrix.

You must specify the dimension of the matrix in the READ command. The first K gives the number of rows, the

second K the number of columns. The M is the matrix identifier for storage. If a file name is not used, READ is

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followed by data lines, each containing one row of the matrix. The following command creates the following

matrix.

Command

READ 3 4 M2

1 2 3 4

5 6 7 8

9 10 11 12

END

Matrix

1 2 3 4

5 6 7 8

9 10 11 12

FILE "filename"

FILE K

Reads or inserts data from the specified text file.

CONCATENATE: Session command for combining text columns

CONCATENATE C...C C

Combines text columns C...C and places into C, to form longer words.

In the following example, portions of numbers were originally entered into columns C1–C3. CONCATENATE

combines C1-C3 into one column, C4.

READ C1-C3;

FORMAT (A4, A3, A4).

192-42-7777

123-45-6789

END

CONCATENATE C1-C3 C4

The worksheet is changed as follows.

C4-TC3-TC2-TC1-T

192-42-7777777742-192-

123-45-6789678945-123-

CODE: Session command for changing values in columns to new

values

CODE (K...K K ... (K...K)K C...C [C...C]

Copies columns, changing the indicated values. For example, the following command language codes every –1

and –2 to 100.

CODE (-1 -2)100 C1 C2;

AFTER.

The command language places the results at the end of the worksheet (C2), as follows.

C2C1

100.0-1.0

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C2C1

3.03.0

1.11.1

100.0-1.0

0.00.0

100.0-2.0

2.42.4

An interval can be abbreviated with a colon. For example, the following command language changes all values

in the range 1 to 1.5, and the value 2 into a 5.

CODE (1:1.5, 2)5 C1-C3 C11-C15

You can make several changes at one time. For example, suppose the data in columns C1–C5 include integers 1

through 10. In this case, the following command language changes all values in C1–C5 that are from 1 to 5 into

10, and all values that are from 6 to 10 into 20, and stores the results in C11–C15.

CODE (1:5)10 (6:10)20 C1-C5 C11-C15

CODE can change values into the missing data code. For example, to change –99 to *, use the following command

language.

CODE (–99) '*' C1-C10;

ORIGINAL.

You can also code text data. Enclose text values with double quotation marks. Denote a missing text value as two

double quotation marks, with no space in between, as "". For example, use the following command language to

code 1, 2, and 3 to low, medium, and high.

CODE (1) "low" (2) "medium" (3) "high" C1 C2

You can also use CONVERT on page 121 to recode text data.

ENDPOINTS K

For coding ranges of values, the value of K controls inclusion of the endpoints.

Endpoints that are included in the rangeValue of K

Lower endpoint only1

Upper endpoint only2

Both endpoints3 (default)

Neither endpoint4

TSUMMARY

Shows the summary table in the output.

TSUMMARY and NODEFAULT are mutually exclusive.

NODEFAULT

Suppresses the summary table in the output.

TSUMMARY and NODEFAULT are mutually exclusive.

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LFPERCENT K K

Recodes the unique values (of the input columns specified in the CODE command) above the K

percentile

in count to K, and places the recoded values into output columns specified in the CODE command.

LFVALUE K K

Recodes the unique values (of the input columns specified in the CODE command) that occur less than K

times to K, and places the recoded values into output columns specified in the CODE command.

Storage

The following subcommands are optional, and are mutually exclusive. If you do not use one of these subcommands,

results are stored in the columns that you specify with the main command.

Note You cannot use NEWWS and AFTER in a local macro.

NEWWS

NEWWS "text"

NEWWS K

Stores the results in a new worksheet. If you do not specify a worksheet name in a "text" argument or in a constant

(K) argument, then Minitab uses the default naming of worksheets.

AFTER

AFTER "text"

Stores the results at the end of the specified worksheet. If you do not specify a worksheet name in a "text"

argument, then Minitab stores the copied data at the end of the active worksheet.

ORIGINAL

Stores the results in the original columns.

CONVERT: Session command for converting text data to numeric

data, and numeric data to text data

CONVERT C C C C

Using the conversion table in C C, converts C to C.

A conversion table, assigning a numeric value to each text value, must be put into two columns before CONVERT

is used. Then, using these matching values, CONVERT changes all corresponding numeric values to the correct

text values (or vice versa). If no match is found, a missing value is stored. Missing values are denoted by * in a

numeric column and a blank in a text column. CONVERT can also be used to convert from numeric to numeric or

from text to text.

Use the following commands to store a conversion table in C1–C2. For more information on entering data using

format statements, go to READ data into a matrix on page 43 or READ data into columns on page 41.

READ C1 C2;

FORMAT (F1, 1X, A6).

1 RED

2 YELLOW

END

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Use the conversion table to do two conversions. The first CONVERT converts numbers in C3 to colors in C10. The

second converts colors in C4 to numbers in C11.

CONVERT C1 C2 C3 C10

CONVERT C2 C1 C4 C11

The worksheet is changed as follows.

C11C10-TC4-TC3C2-TC1

1REDRED1RED1

1YELLOWRED2YELLOW2

2REDYELLOW1

1RED3

*YELLOWBLUE2

NODEFAULT

Suppresses the summary table in the output.

DROUND: Session command for rounding date/time values

DROUND C C

Rounds the original date/time values in the first C and stores them in the second C.

For example, suppose you have sales data for each day of the year, but you want to summarize sales for each

quarter of the year. You can use Round Date/Time to round the dates down to the nearest quarter.

Units for rounding

Specify the unit to round to. You must use one of these mutually exclusive subcommands.

YEAR

QUARTER

MONTH

WEEK

DAY

HOUR

MINUTE

SECOND

TENTH

HUNDREDTH

THOUSANDTH

Options

DROUND rounds down to the nearest whole unit.

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Use UP to round up to the nearest whole unit.

NEAREST

Use NEAREST to round up or down to the nearest whole unit.

NUMERIC: Session command for changing the data format of a

date/time column or extracting date/time components

NUMERIC C C

The NUMERIC command serves two functions:

•

To change a date/time or a text column to a numeric column. (Use with a date/time or text input column.)

•

To extract one or more components (such as the day, quarter, or hour) from a date/time column, and save

those components in a numeric column. (Use with a date/time input column.)

Note When the input column is text, no subcommands are available.

Date/time input

WKDAY

Extracts the day of the week (Sun, Mon, Tue, Wed, Thu, Fri, Sat).

DAY

Extracts the day of the month (01, 02, ..., 31).

WEEK

Extracts the week of the year number (Wk01 - Wk53).

MONTH

Extracts the month (Jan, Feb, Mar, Apr, ..., Dec).

QUARTER

Extracts the quarter (Q1, Q2, Q3, Q4).

YEAR

Extracts the year.

TWODIGIT

Uses the two digit format (00, 01, ..., 99).

FOURDIGIT

Uses the four digit format (2000, 2001, ..., 2099).

HOUR

Extracts the hour (00, 01, ..., 23).

MINUTE

Extracts the minute (00, 01, ..., 59).

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SECOND

Extracts the second (00, 01, ..., 59).

TENTHS

Extracts the tenths of a second (0, 1, 2, ..., 9).

HUNDREDTHS

Extracts the hundredths of a second (0, 1, ..., 99).

THOUSANDTHS

Extracts the thousandths of a second (0, 1, 2, ..., 999).

TEXT: Session command for changing the data type of a column

to text

TEXT C...C C...C

Changes data type of C...C to text and places results in C...C. The TEXT command serves the following functions:

•

Use TEXT to change a date/time or a numeric column to a text column. (Use with a date/time or numeric input

column.)

•

Use TEXT to extract one or more components (such as the day, quarter, or hour) from a date/time column,

and save those components in a text column. (Use with a date/time input column.)

•

Use TEXT to format the width of a text column. (Use with a text input column.)

Minitab automatically formats the new alpha column up to 8 characters wide. Use WIDTH or MAXWIDTH (you

cannot use both) to override this default.

Date/time input column

WKDAY

Extracts the day of the week (Sun, Mon, Tue, Wed, Thu, Fri, Sat).

DAY

Extracts the day of the month (01, 02, ..., 31).

WEEK

Extracts the week of the year number (Wk01 - Wk53).

MONTH

Extracts the month (Jan, Feb, Mar, Apr, ..., Dec).

QUARTER

Extracts the quarter (Q1, Q2, Q3, Q4).

YEAR

Extracts the year.

TWODIGIT

Uses the two digit format (00, 01, ..., 99).

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FOURDIGIT

Uses the four digit format (2000, 2001, ..., 2099).

HOUR

Extracts the hour (00, 01, ..., 23).

MINUTE

Extracts the minute (00, 01, ..., 59).

SECOND

Extracts the second (00, 01, ..., 59).

TENTHS

Extracts the tenths of a second (0, 1, 2, ..., 9).

HUNDREDTHS

Extracts the hundredths of a second (00, 01, ..., 99).

THOUSANDTHS

Extracts the thousandths of a second (000, 001, 002, ..., 999).

WIDTH K

Fixes the width (in characters) of the output column.

MAXWIDTH K

Fixes the maximum width (in characters) of the output column.

MISSING "text"

Converts missing values to "text".

Numeric input column

WIDTH K

Fixes the width (in characters) of the output column.

MAXWIDTH K

Fixes the maximum width (in characters) of the output column.

SIGNIFICANT K

Specifies the number of significant digits to maintain (up to a maximum of 6). You cannot use both SIGNIFICANT

and DECIMALS with one command.

DECIMALS K

Specifies the number of decimal digits to maintain. You cannot use both SIGNIFICANT and DECIMALS with one

command.

MISSING "text"

Converts the missing value symbol * to a specified character string. Unless you specify the width of your output

with WIDTH or MAXWIDTH, the default length for the string is 8 characters.

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Text input column

WIDTH K

Fixes the width (in characters) of the output column.

MAXWIDTH K

Fixes the maximum width (in characters) of the output column.

STACK: Session command for stacking blocks of columns and

constants on top of each other

STACK (E...E) C

STACK (E...E) ... (E...E) (C...C)

Stacks blocks of columns and constants on top of each other.

The following command language stacks C3 and C4 on C1 and C2.

STACK (C3 C4) (C1 C2) (C5 C6)

Columns to stack

C4C3C2C1

1557013066

1456612564

1606911565

Stacked columns

C6C5

15570

14566

16069

13066

12564

11565

The following command language puts 66, 64, 65, 70, 66, 69, 67, and 63 into C10. The last column (or block of

columns) specified is the target column.

STACK C1 C3 67 63 C10

SUBSCRIPTS C

Creates a column of subscripts in the original worksheet. The first block is given the subscript 1, the second

block the subscript 2, and so on.

USENAMES

Creates subscripts based on variable names.

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NEWWS

NEWWS "text"

NEWWS K

Stores the results in a new worksheet. If you do not specify a worksheet name in a "text" argument or in a

constant (K) argument, then Minitab uses the default naming of worksheets. Minitab automatically creates

a subscripts column in the new worksheet.

Important You cannot use NEWWS in a local macro. For more information, go to Session commands that are not allowed in

macros on page 1179.

USENAMES

Creates subscripts based on variable names.

UNSTACK: Session command for separating a column into multiple

columns

UNSTACK (C...C) (E...E)...(E...E)

Separates one or more columns into several blocks of columns and stored constants, based on subscript values

stored in an associated column.

Unstack is useful for subsetting data.

SUBSCRIPTS C...C

Uses columns to unstack data. The rows with the smallest subscript are stored in the first block, the rows

with the second smallest subscript in the second block, and so on. If you do not use SUBSCRIPTS, each row

is stored in a separate block.

In general, each block must be enclosed in parentheses. However, if a block contains just one argument, you

may omit the parentheses.

For most applications, the subcommand SUBSCRIPTS is needed.

MISSINGS

Includes rows subscripted with missing data as part of the unstacked data.

NEWWS

NEWWS "text"

NEWWS K

Stores the results in a new worksheet. If you do not specify a worksheet name in "text" or in a constant (K)

argument, then Minitab uses the default naming of worksheets.

Important You cannot use NEWWS in a local macro. For more information, go to Session commands that are not allowed in

macros on page 1179.

VARNAMES

Labels the columns for the unstacked data with the source variable name (the source column's label or

number if no label exists) and the corresponding row entries from the subscript column or columns. If you

do not use SUBSCRIPT, the text strings "Row1", "Row2", and so on are used.

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AFTER

Appends the unstacked data into empty columns after the last column of data in the current worksheet.

Important You cannot use AFTER in a local macro. For more information, go to Session commands that are not allowed in

macros on page 1179.

WSTACK: Session command for stacking worksheets

WSTACK

Important You cannot use WSTACK in a local macro. For more information, go to Session commands that are not allowed in macros

on page 1179.

Use WSTACK to stack two or more worksheets. The argument is the names of the worksheets. For example,

WSTACK "Worksheet1" "Worksheet 2" stacks Worksheet 1 on top of Worksheet 2. Minitab stacks columns with

the same names. Columns that don't have the matching names are kept as separate columns.

Within this command, you can specify that the results are placed in a new worksheet (NEWWS) or appended to

an existing worksheet (APPEND). NEWWS and APPEND subcommands are mutually exclusive. If neither is specified,

NEWWS is assumed.

You can use MERGE on page 106 to combine worksheets side-by-side.

NEWWS "text"

NEWWS K

Stores the results in a new worksheet. If you do not specify a worksheet name in "text" or in a constant (K)

argument, then Minitab uses the default naming of worksheets.

APPEND

Appends the data to the bottom of the first worksheet that you specified in WSTACK.

SOURCE ["column name"]

SOURCE appends the source worksheet name to the bottom of the specified column. If you do not

specify the column, the command creates a new ID column in the output sheet.

TSUMMARY (default)

TSUMMARY and NODEFAULT are mutually exclusive. TSUMMARY shows the summary table in the output.

NODEFAULT

TSUMMARY and NODEFAULT are mutually exclusive. NODEFAULT suppresses the summary table in the

output.

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TRANSPOSE: Session command for changing rows to columns,

and columns to rows

TRANSPOSE C...C

TRANSPOSE C M

TRANSPOSE M C

TRANSPOSE M [M]

TRANSPOSE reconfigures data so that rows become columns and columns become rows.

•

TRANSPOSE C...C transposes the values in the columns.

•

TRANSPOSE C M transposes the values in a column to a matrix.

•

TRANSPOSE MC transposes the values in a single row matrix to a column.

•

TRANSPOSE M [M] transposes the values in a matrix to another matrix.

VARNAMES C

Specifies a column that contains variable names for the transposed columns.

STORE C...C

STORE M

Specifies where the transposed data are to be written. You can store the data in columns or a matrix. When

you transpose data to columns, the number of columns specified must equal the number of rows in the

original columns.

If you do not use STORE, the data are stored in a new worksheet.

LABELS C

Specifies the column in which the labels of the transposed column or columns or matrix are stored.

NEWWS

NEWWS "text"

NEWWS K

Stores the results in a new worksheet. If you do not specify a worksheet name in "text" or in a constant (K)

argument, then Minitab uses the default naming of worksheets.

Important You cannot use NEWWS in a local macro. For more information, go to Session commands that are not allowed in

macros on page 1179.

AFTER

Appends the transposed data into empty columns after the last column of data in the current worksheet.

Important You cannot use AFTER in a local macro. For more information, go to Session commands that are not allowed in

macros on page 1179.

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COPY: Session command for copying data

COPY [E...E E...E]

Copy specified columns, constants, and matrices, or a subset thereof, into a similar or compatible format in a

specified worksheet.

In addition to format, the source and destination must be compatible in size. For example, you cannot copy a

column with 100 rows into a set of 50 constants. You can subset the source data to accommodate the size of the

destination.

If you don't specify an argument, COPY copies the entire contents of the current worksheet to another worksheet.

You can specify an argument to do the following:

•

Copy columns into columns, constants, or a matrix.

•

Copy constants into constants or columns.

•

Copy a matrix into columns or a matrix.

Specify a subset

INCLUDE and EXCLUDE are mutually exclusive. The last one issued is honored. Choose INCLUDE or EXCLUDE based

on the degree of subsetting. For example, if you want to use all rows, except a few, use EXCLUDE and specify a small

number of rows with the ROWS subsubcommand.

INCLUDE

Includes the specified rows.

BRUSHED

Use to specify all brushed rows for subsetting.

WHERE "expression"

Specify a condition to be met for subsetting. This condition must be in double quotation marks. For example:

•

To subset where C1 is 2, enter WHERE "C1 = 2".

•

To subset where C2 = a, enter WHERE "C2 = ""a"".

•

To subset where C3 = 3/3/16, enter WHERE "C3 = DATE(""3/3/16"")".

ROWS K:K

List all row numbers to be included or excluded with a space between each. Denote a patterned range by

K:K/K. For example, 10:50/5 denotes all values from 10 to 50 by intervals of 5.

EXCLUDE

Excludes the specified rows.

BRUSHED

Use to specify all brushed rows for subsetting.

WHERE "expression"

Specify a condition to be met for subsetting. This condition must be in double quotation marks. For example:

•

To subset where C1 is 2, enter WHERE "C1 = 2".

•

To subset where C2 = a, enter WHERE "C2 = ""a"".

•

To subset where C3 = 3/3/16, enter WHERE "C3 = DATE(""3/3/16"")".

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ROWS K:K

List all row numbers to be included or excluded with a space between each. Denote a patterned range by

K:K/K. For example, 10:50/5 denotes all values from 10 to 50 by intervals of 5.

USE [C] K...K

Selects rows to copy.

Specify target location

Important NEWWS, AFTER, and STORE cannot be used in local macros. For more information, go to Session commands that are not

allowed in macros on page 1179.

Note You can use only one NEWWS, AFTER, or STORE subcommand.

NEWWS

NEWWS "text"

NEWWS K

Stores the results in a new worksheet. If you do not specify a worksheet name in "text" or in a constant (K) argument,

then Minitab uses the default naming of worksheets.

AFTER

AFTER "text"

AFTER K

Stores the results at the end of the specified worksheet. If you do not specify a worksheet name in a "text" argument

or in a constant (K) argument, then Minitab stores the copied data at the end of the active worksheet.

STORE K E...E

STORE "text" E...E

Stores the copied data in the specified columns constants, or matrices of the worksheet named in a "text" argument

or in a constant (K).

Naming

VARNAMES

Gives default names to the stored columns, constants, or matrix.

RMERGE: Session command for merging worksheets

RMERGE K...K

Use RMERGE to combine two or more worksheets side-by-side into a new worksheet. The argument is the names

of the worksheets. For example, RMERGE "Worksheet1" "Worksheet 2" merges Worksheet 2 to the right

of Worksheet 1.

Note Stored constants, matrices, DOE objects, and worksheet descriptions do not transfer into the merged worksheet.

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NAME K (optional)

Specifies the name K for the new worksheet.

Editor

CFORMAT: Session command for conditional formatting of

worksheet cells

CFORMAT C...C

Formats cells in the specified columns based on the specified rule.

You can specify only one rule subcommand (and possibly its sub-subcommands) with the CFORMAT command

at a time. However, you can run CFORMAT on the same column multiple times, and the rules accumulate. If a cell

is affected by multiple rules, the formatting of the most recent rule is used.

The availability of a specific rule might depend on the data type of the column specified in CFORMAT.

Attribute subcommands

The following subcommands define the cell format for cells that meet the condition.

TCOLOR K

Specifies the text color with a number value in K, which is a number from Numbers for colors to use in session

commands on page 1172.

BOLD

Applies bold style to text in the cells to format.

NOBOLD

Removes bold style from text in the cells to formats.

ITALIC

Applies italic style to text in the cells to format.

NOITALIC

Removes italic style from text in the cells to format.

UNDERLINE

Underlines text in the cells to format.

NOUNDERLINE

Removes underline from text in the cells to format.

COLOR K

Specifies the fill color of the cells with a number value in K, which is a number from Numbers for colors to use in

session commands on page 1172.

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Rules for highlighting cells

MISSING

Formats cells that contain missing observations (available for columns with any data type).

GT K

Formats cells that are greater than K (available for columns with any data type).

LT K

Formats cells that are less than K (available for columns with any data type).

BETWEEN K K

Formats cells between K and K, inclusive (available for columns with any data type).

NOTBETWEEN K K

Formats cells that are not between K and K, inclusive (available for columns with any data type).

EQUALS K

Formats cells that are equal to K (available for columns with any data type).

TEXT "text"

Formats cells that contain the text string, 'text" (available for text columns) The text string may be found within a

larger text string. For example, if the rule is to format cells that contain "abc", a cell that contains dabcd would

be formatted.

VALUES C

VALUES K...K

Formats cells that match any of the values in C or in K...K (available for columns with any data type) C or K...K have

the same data type as the columns specified in CFORMAT.

NOVALUES C

NOVALUES K...K

Formats cells that do not match any of the values in C or in K...K (available for columns with any data type) C or

K...K have the same data type as the columns specified in CFORMAT.

YESTERDAY

Formats cells that contain yesterday's date (available for date/time columns).

TODAY

Formats cells that contain today's date (available for date/time columns).

LAST K

Formats cells that contain a date that occurred within the last K days (available for date/time columns).

LWEEK

Formats cells that contain a date that occurred last week (available for date/time columns).

TWEEK

Formats cells that occur this week (available for date/time columns).

LMONTH

Formats cells that occurred last month (available for date/time columns).

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TMONTH

Formats cells that occur this month (available for date/time columns).

Hi low rules

HVALUE K

Formats cells that contain the highest K values in the column (available for numeric and date/time columns). If

there are ties at the K

location, all tied values will be formatted.

HPERCENT K

Formats cells that contain the highest K percentage of values in the column (available for numeric and date/time

columns).

LVALUE K

Formats cells that contain the lowest K values in the column (available for numeric and date/time columns) If

there are ties at the K

location, all tied values will be formatted.

LPERCENT K

Formats cells that contain the lowest K percentage of values in the column (available for numeric and date/time

columns).

Pareto rules

MFREQUENT K

Formats cells that contain the K most frequent values (available for columns with any data type).

MPERCENT K

Formats cells that contain the top K% of values (available for columns with any data type).

BFREQUENT

Formats cells that contain the K least frequent values (available for columns with any data type).

BPERCENT K

Formats cells that contain the bottom K% of values (available for columns with any data type).

Statistical rules

OUTLIER

Formats cells that are outliers (available for columns with any data type) Columns can be different data types.

Outliers are determined based on one of the following sub-subcommands:

BOXPLOT

Observations that meet the conditions for outliers on a boxplot are formatted.

STDEV K

Observations that are more than K standard deviations away from the mean are formatted.

CONTROL K K

Formats cells that contain values that are out of control (available for numeric and date/time data).

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The first K specifies the rule:

•

K = 1 if data are continuous.

•

K = 2 if the data are attribute.

The second K specifies the subgroup size. K > 0.

Examples of subcommand syntax:

•

CONTROL 1 1 specifies to use I chart test 1.

•

CONTROL 1 K, where K > 1, specifies to use XBar chart test 1.

•

CONTROL K K, where K > 0, specifies to use P chart test 1.

The CONTROL subcommand honors preferences (File > Options) settings to determine the Test 1 rule criteria

and to determine the estimate of standard deviation used.

SPECIFICATION

Formats cells that are outside of the specification limits (available for numeric and date/time data)

Specify one or both of the following:

LSPEC K

Specifies the lower specification limit. Cells with values less than K are out of specification.

K for LSPEC can be any numeric or date/time value, but K for LSPEC must be less that K for USPEC. Both Ks

must match the type for the argument to CFORMAT.

USPEC K

Specifies the upper specification limit. Cells with values greater than K are out of specification.

K for USPEC can be any numeric or date/time value, but K for LSPEC must be less that K for USPEC. Both Ks

must match the type for the argument to CFORMAT.

RESIDUAL

Formats cells that are unusual observations from a model because of large standardized residual (available for

response columns that have a model associated with them).

UNUSUAL

Formats cells that are unusual observations from a model because they exhibit high leverage (available for response

columns that have a model associated with them).

CFAUTOMATICALLY: Session command for automatically

recalculating values

CFAUTOMATICALLY

Updates the data by automatically recalculating the values in columns or stored constants with assigned formulas

whenever you change the corresponding data for variables. To update formulas manually, use CFMANUALLY on

page 136.

This command cannot be used in a local macro.

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CFMANUALLY: Session command for manually recalculating values

Allows you to manually recalculate the values in columns or stored constants with assigned formulas only when

using the command CFNOW on page 136 (Calculate All Formulas Now). To update formulas automatically whenever

the data they depend on changes, use CFAUTOMATICALLY on page 135.

This command cannot be used in a local macro.

CFNOW: Session command for recalculating values now

CFNOW

Recalculates the values in columns or stored constants with assigned formulas using the current data for variables.

Can be used only when Calculate All Formulas Automatically is deselected. For more information about

calculating formulas manually with command language, go to CFMANUALLY on page 136.

This command cannot be used in a local macro.

FDATE/TIME: Session command for changing the format of

date/time columns

FDATE/TIME C...C

Changes the format of a date/time columns C...C. You can use FDATE/TIME for columns that are empty.

For more information on date/time formats, go to Default date/time formats on page 1140.

FORMAT (format statement)

Indicates the format of the date/time column. For example, use the subcommand FORMAT (DTm/d/yy) to

format the column as 5/25/18. Use the subcommand FORMAT (DTm-d-yy) to format the new column as

5-25-18.

For multiple columns, add the number of columns to the format statement. For example, for 2 columns, use

FORMAT (2DTm-d-yy).

FNUMERIC: Session command for changing columns to numeric

format

FNUMERIC C...C

Changes the format of columns C...C to the specified numeric format. You can use FNUMERIC for columns that

are empty.

AUTO

The values in the column determine the format of the column, for example, the number of decimal places

and the currency symbol (if entered).

FIXED K

Displays numeric data to K decimal places.

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EXPONENTIAL [K]

Displays numeric data using exponential notation. The optional argument K specifies the number of decimal

places.

CURRENCY [K]

Displays currency data. The optional argument K specifies the number of decimal places, from 0 to 30 inclusive.

SYMBOL "text"

Specifies the currency symbol as a text value.

NEGATIVE K

Displays negative values using a minus sign (K = 1) or parentheses (K = 2).

PERCENTAGE K

Displays percentage data to K decimal places.

FTEXT: Session command for changing the format of text columns

FTEXT

Changes the format of text columns C...C. You can use FTEXT for empty columns.

FORMULA: Session command for assigning a formula to a column

FORMULA E = expression

Assigns a formula to a column or stored constant. The expression that defines the formula may contain arithmetic

operations, comparison operations, logical operations, and functions. For a list of the functions you can use, go

to LET: Session command for correcting a number in a worksheet or performing arithmetic on page 63.

This command cannot be used in a local macro.

RFORMULA: Session command for removing formulas

RFORMULA E...E

Removes formulas from the columns or constants that you specify. Removing a formula from a column does not

remove the data from the column.

You cannot use this command in a local macro.

VORDER: Session command for controlling the order for text

categories to be processed by Minitab commands

VORDERC...C

Use VORDER to control the order in which you would like text categories to be processed by Minitab commands.

By default, text categories are processed in alphabetical order. However, alphabetical order might not always be

the most convenient way to process your data.

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VALUES K...K

VALUES C

Specifies the value order using either stored or stated text constants or a single text column.

If you use the VALUES subcommand more than one time, the last one with valid arguments wins. In the first

form, stored or stated text constants are used to specify the values in order. In the second form, the rows of

a single text column are used to specify the values in order. In either form, it is an error for values to be

repeated. Note that the command language does not provide access to standard orderings.

WORKSHEET

Specifies to order data based on the first occurrence in the worksheet.

ALPHABETICAL

Specifies to order data in alphabetical order.

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Basic Statistics

DESCRIBE: Session command for summarizing

numeric data with statistics

DESCRIBE C...C

Produces descriptive statistics for each column. You can do the calculations and display graphs separately for

each level of a BY variable.

For information on storing many different statistics, go to STATS on page 143.

Control of grouping

Use BY to produce separate statistics and graphs for each unique value in C. The values in the BY column may

contain numeric, text, or date/time data and may be any value. When the BY column is text, the first entry (row

1) is level 1, the next entry that is different is level 2, and so on. Column lengths must be equal to use BY.

Statistics

MEAN

Calculates the arithmetic mean, or average. The mean is a commonly used measure of the center of a batch of

numbers.

Note Missing values are omitted from the calculation of the function Mean.

SEMEAN

Gives the standard error of the mean. It is calculated as StDev / SQRT (N).

STDEV

Calculates the sample standard deviation, which provides a measure of how spread out the data are. To calculate

the variance, simply square the standard deviation value.

If the column contains x1, x2, ..., xn, with mean x, then the standard deviation is calculated as follows:

Note Missing values are omitted from the calculation of the function Standard deviation.

VARIANCE

Variance is a measure of how far the data are spread about the mean. Sample variance equals the standard

deviation squared. It can also be computed with this formula:

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CVARIATION

Displays the coefficient of variation, a measure of relative variability. It is calculated as: 100 (s / x).

MEDIAN

Stores the median of a column. The median is in the middle of the data: half the observations are less than or

equal to it. Suppose the column contains n values. If n is odd, the median is the value in the middle. If n is even,

the median is the average of the two middle values.

Note Missing values are omitted from the calculation of the function Median.

MODE

Displays the mode of a column. The mode of a data set is the value that occurs most frequently. For example, the

mode for data set {1, 3, 4, 4, 7} is 4.

Note For data sets that have multiple mode values, Minitab displays only the four smallest modes.

TRMEAN

A 5% trimmed mean is calculated. Minitab removes the smallest 5% and the largest 5% of the values (rounded

to the nearest integer).

SUM

Calculates the sum.

Note Missing values are omitted from the calculation of the function Sum.

MIN

Stores the smallest number in a column.

Note Missing values are omitted from the calculation of the function MIN.

MAX

Stores the largest number in a column.

Note Missing values are omitted from the calculation of the function MAX.

RANGE

Calculates the difference between the largest and smallest data value.

SSQ

Squares each value in the column, and computes the sum of those squared values. That is, if the column contains

, x

, ..., x

, then sum of squares calculates (x

+ x

+ ... + x

Note Missing values are omitted from the calculation of the function the function Sum of Squares (corrected).

SKEWNESS

Skewness is a measure of asymmetry. A value more than or less than zero indicates skewness in the data. But a

zero value does not necessarily indicate symmetry.

KURTOSIS

Kurtosis is one measure of how different a distribution is from the normal distribution. A positive value characterizes

a distribution with heavier tails than the normal distribution. A negative value characterizes a distribution with

lighter tails than the normal distribution.

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MSSD

MSSD computes half the Mean of the Squared Successive Differences of a batch of numbers. For example, suppose

a column contains 1, 2, 4, and 10. The successive differences are 2 - 1 = 1, 4 - 2 = 2, and 10 - 4 = 6. Then:

MSSD = mean(1-squared, 2-squared, and 6-squared) / 2 = 6.83333

Returns the number of nonmissing observations in a column.

NMISS

Returns the number of missing observations in a column.

COUNT

Returns the total number of observations in a column.

When used as an option with TALLY on page 903 (Stat > Tables > Tally > Tally Individual Variables) or the

STATS on page 143, COUNT computes the number of observations in each group. For example, if you tally a column

containing 1s, 2s and 3s, COUNT tells you how many of each there are. Using COUNTS with STATS, you can store

these counts, which is useful if you want to generate a frequency plot.

CUMN

CUMN, or cumulative n, computes a cumulative frequency count of the number of non-missing observations in

each group listed in the BY columns. If there are no BY columns listed, CUMN simply counts the non-missing

observations in the columns listed with DESCRIBE or STATS. CUMN counts observations for non-missing groups

only, unless you include the MISSING subcommand.

PERCENT

If you want statistics for different groups listed in one or more BY columns, PERCENT computes what percentage

of the whole is accounted for by each group.

For example, suppose you list one BY column containing five 1s, one 2, and four 3s. PERCENT computes 50% for

the first group, 10% for the second, and 40% for the third.

If you omit the BY subcommand, then PERCENT and CUMPERCENT calculate the value 100 (for 100%).

If you include columns on the main DESCRIBE or STAT command line, then PERCENT and CUMPERCENT calculate

percentages for only the non-missing values in those columns.

CUMPERCENT

If you want statistics for different groups listed in one or more BY columns, CUMPERCENT computes the cumulative

percentage represented by each group.

For example, suppose you list one BY column containing five 1s, one 2, and four 3s.

If you omit the BY subcommand, then PERCENT and CUMPERCENT calculate the value 100 (for 100%).

If you include columns on the main DESCRIBE or STAT command line, then PERCENT and CUMPERCENT calculate

percentages for only the non-missing values in those columns.

Graphs

Each graph subcommand displays a separate graph for each variable listed on the command line.

If you use the BY subcommand, Minitab generates one graph for each column listed on the command line.

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GHISTOGRAM

Displays a histogram for each variable.

GHISTOGRAM and GNHISTOGRAM display a histogram for each level of the BY variable in a separate panel of

the graph. The histograms for one variable are on the same scale to facilitate comparisons between the levels.

GNHISTOGRAM

Displays a histogram with a normal curve for each variable. GNHISTOGRAM bases each normal curve on the

sample mean and standard deviation of the data in the corresponding histogram.

GHISTOGRAM and GNHISTOGRAM display a histogram for each level of the BY variable in a separate panel of

the graph. The histograms for one variable are on the same scale to facilitate comparisons between the levels.

GINDPLOT

Displays an individual value plot for each variable.

GINDPLOT and GBOXPLOT display side-by-side plots in a single graph, one for each level of the BY variable. This

allows you to easily compare the different levels of the BY variable.

GBOXPLOT

Displays a boxplot for each variable.

GINDPLOT and GBOXPLOT display side-by-side plots in a single graph, one for each level of the BY variable. This

allows you to easily compare the different levels of the BY variable.

GSUMMARY

Displays a graphical summary for each variable.

Quartiles

Every group of data has three quartiles. To calculate quartiles, use Sort (Data > Sort) to order the data from smallest

to largest. The first quartile (Q1) is the observation at position (n+1) / 4. The second quartile is the median. The third

quartile (Q3) is the observation at position 3(n+1) / 4, where n is the number of observations. If the position is not an

integer, interpolation is used.

For example, suppose n=10. Then (10 + 1)/4 = 2.75, and Q1 is between the second and third observations (call them

x2 and x3), three-fourths of the way up. Thus, Q1 = x2 + 0.75(x3 – x2). Since 3(10 + 1)/4 = 8.25, Q3 = x8 + 0.25(x9 –

x8), where x8 and x9 are the eight and ninth observations.

QONE

The first quartile is also referred to as the 25

percentile because data from 25% of the observations are less than

or equal to this value.

IQRANGE

The interquartile range equals Q3 – Q1.

QTHREE

The third quartile is also referred to as the 75

percentile because data from 75% of the observations are less

than or equal to this value.

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STATS: Session command for storing descriptive

statistics

STATS [C...C]

STATS computes a wide range of statistics on entire columns or subsets of columns and stores those statistics in

the worksheet.

STATS is similar to the commands TALLY on page 903, TABLE on page 901, and DESCRIBE on page 139. TALLY, TABLE,

and DESCRIBE compute many of these statistics and display them in an easy-to-read table format. But TABLE and

DESCRIBE cannot store the values of these statistics for use in further analysis as STATS can.

Here is an example using the PULSE.MTW data set. This data set includes three columns named Height, Weight,

and Sex (containing 1's for males, 2's for females). The NAME command names the three new columns that STATS

will store its results in. GVALUES will store the numbers 1 and 2 into C11, because these are the distinct values in

the BY column named Sex. MEAN will store the mean height for males and the mean height for females in C12,

and the mean weight for males and the mean weight for females in C13.

NAME C11 'SexID' C12 'MeanHt' C13 'MeanWt'

STATS 'Height' 'Weight';

BY 'Sex';

GVALUES C11;

MEAN C12 C13.

When there is no BY subcommand, you must list at least one column on the main STATS command line. In that

case, STATS computes the requested statistics for the entire column.

When you omit columns from the main STATS command line, you must use the BY subcommand. In that case,

you may store these statistics: N, NMISS, COUNT, CUMN, PERCENT, and CUMPERCENT. For more information,

go to Notes on subcommands that store descriptive statistics (STATS command) on page 1172.

You can use either the GLABELS, GVALUES, or GIDS subcommand so that you know which group each row of

statistics belongs to. Use the NAME on page 55 command to name each column so that you know what you

stored.

Control of grouping

BY C...C

BY lists the columns that contain the group variables (such as a column named Temp containing the values Low,

Medium, and High). Columns listed with BY may contain numeric, text, or date/time data. When you include the

BY subcommand, STATS computes statistics for each group listed in the BY columns or columns. When you omit

the BY subcommand, STATS computes statistics for whole columns rather than for subgroups.

NOEMPTY

NOEMPTY omits empty cells.

MISSINGS

MISSINGS includes missing as a distinct value of the BY column (when the BY column includes a missing value).

Otherwise, rows with missing values in BY columns are omitted.

Options

These subcommands store each distinct value listed in each BY column. Therefore, use the same number of columns

as you list with BY. It is a good idea to include at least one of these subcommands so you will know which group each

row of statistics belongs to.

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GLABELS C...C

GLABELS stores the actual labels from your BY columns (such as 10, 20, 30, or Low, Medium, High). GLABELS

always stores the labels as text.

GVALUES C...C

GVALUES also stores the actual labels from your BY columns, but it stores them as the same data type (numeric

or text) as the BY variable.

GIDS C...C

GIDS stores a 1 for the first value in your BY column, a 2 for the second value, and so on, in a numeric column. If

the BY column is numeric, GIDS stores a 1 for the smallest number in the BY column, a 2 for the next largest

number, and so on. If the BY column contains text, GIDS stores a 1 for the first entry (row 1), a 2 for the next entry

that is different, and so on.

EXPAND

Stores a row of the desired statistic for each row of input instead. If you do not issue EXPAND, Minitab stores the

statistic in the first row.

Statistics

MEAN

Stores the arithmetic mean, or average. The mean is a commonly used measure of the center of a batch of numbers.

Note Missing values are omitted from the calculation of the function Mean.

SEMEAN

Stores the standard error of the mean. It is calculated as StDev / SQRT (N).

STDEV

Stores the sample standard deviation, which provides a measure of how spread out the data are. To calculate the

variance, simply square the standard deviation value.

If the column contains x1, x2, ..., xn, with mean x, then the standard deviation is calculated as follows:

Note Missing values are omitted from the calculation of the function Standard deviation.

VARIANCE

Stores the sample variance, which is a measure of how far the data are spread about the mean. Sample variance

equals the standard deviation squared. It can also be computed with this formula:

CVARIATION

Stores the coefficient of variation, a measure of relative variability. It is calculated as: 100 (s / x).

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MEDIAN

Stores the median of a column. The median is in the middle of the data: half the observations are less than or

equal to it. Suppose the column contains n values. If n is odd, the median is the value in the middle. If n is even,

the median is the average of the two middle values.

Note Missing values are omitted from the calculation of the function Median.

TRMEAN

Stores a 5% trimmed mean. Minitab removes the smallest 5% and the largest 5% of the values (rounded to the

nearest integer).

SUMS

Stores the sum.

Note Missing values are omitted from the calculation of the function Sum.

MIN

Stores the smallest number in a column.

Note Missing values are omitted from the calculation of the function MIN.

MAX

Stores the largest number in a column.

Note Missing values are omitted from the calculation of the function MAX.

RANGE

Stores the difference between the largest and smallest data value.

SSQ

Stores the sum of squares. Squares each value in the column, and computes the sum of those squared values.

That is, if the column contains x

, x

, ..., x

, then sum of squares calculates (x

+ x

+ ... + x

Note Missing values are omitted from the calculation of the function the function Sum of Squares (corrected).

SKEWNESS

Stores the skewness value. Skewness is a measure of asymmetry. A value more than or less than zero indicates

skewness in the data. But a zero value does not necessarily indicate symmetry.

KURTOSIS

Stores the kurtosis value. Kurtosis is one measure of how different a distribution is from the normal distribution.

A positive value characterizes a distribution with a heavier tails than the normal distribution. A negative value

characterizes a distribution with lighter tails than the normal distribution.

MSSD

Stores half the Mean of the Squared Successive Differences of a batch of numbers. For example, suppose a column

contains 1, 2, 4, and 10. The successive differences are 2 – 1 = 1, 4 – 2 = 2, and 10 – 4 = 6. Then:

MSSD = mean(1-squared, 2-squared, and 6-squared) / 2 = 6.83333

Stores the number of nonmissing observations in a column.

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NMISS

Stores the number of missing observations in a column.

COUNT

Stores the total number of observations in a column.

When used as an option with TALLY on page 903 (Stat > Tables > Tally > Tally Individual Variables) or the

STATS command, COUNT computes the number of observations in each group. For example, if you tally a column

containing 1s, 2s and 3s, COUNT tells you how many of each there are. Using COUNTS with STATS, you can store

these counts, which is useful if you want to generate a frequency plot.

CUMN

Stores a cumulative frequency count of the number of non-missing observations in each group listed in the BY

columns. If there are no BY columns listed, CUMN simply counts the non-missing observations in the columns

listed with DESCRIBE or STATS. CUMN counts observations for non-missing groups only, unless you include the

MISSING subcommand.

PERCENT

If you want statistics for different groups listed in one or more BY columns, PERCENT computes what percentage

of the whole is accounted for by each group.

For example, suppose you list one BY column containing five 1s, one 2, and four 3s. PERCENT computes 50% for

the first group, 10% for the second, and 40% for the third.

If you omit the BY subcommand, then PERCENT and CUMPERCENT calculate the value 100 (for 100%).

If you include columns on the main DESCRIBE or STAT command line, then PERCENT and CUMPERCENT calculate

percentages for only the non-missing values in those columns.

CUMPERCENT

If you want statistics for different groups listed in one or more BY columns, CUMPERCENT computes the cumulative

percentage represented by each group.

For example, suppose you list one BY column containing five 1s, one 2, and four 3s.

If you omit the BY subcommand, then PERCENT and CUMPERCENT calculate the value 100 (for 100%).

If you include columns on the main DESCRIBE or STAT command line, then PERCENT and CUMPERCENT calculate

percentages for only the non-missing values in those columns.

GSUMMARY: Session command for displaying a

graphical summary of each variable

GSUMMARY

Displays a graphical summary of each variable.

Lists the columns that contain the group variables (such as a column named Temp containing the values

Low, Medium, and High). Columns listed with BY may contain numeric, text, or date/time data.

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When you include the BY subcommand, GSUMMARY creates a summary for each group listed in the BY

column or columns. When you omit the BY subcommand, GSUMMARY creates a summary for whole columns

rather than for subgroups.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default

value of K is 95.

SMCONF C C

Stores the confidence interval for the median in C and C.

SSCONF C C

Stores the confidence interval for the standard deviation in C and C.

ONEZ: Session command for performing a 1-sample

Z-test

ONEZ C...C

ONEZ K K

Performs a 1-sample Z-test for the raw or summarized data. You need to provide a value for population standard

deviation the SIGMA subcommand. When you do not know the population standard deviation, you can use ONET

on page 148.

ONEZ C...C performs a Z-test for each of the columns in C...C.

ONEZ K K performs a Z-test using summarized data with sample size and sample mean in K and K, respectively.

SIGMA K (required)

Specifies the value of the population standard deviation.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default

value of K is 95.

ALTERNATIVE K

Enter K to specify the direction of the alternative hypothesis.

Format of the alternative hypothesisValue of K

Less than–1

Not equal to0 (default)

Greater than1

TEST K

Tests the null hypothesis using the hypothesized mean specified in K.

SPVALUE C

SPVALUE stores the p-value of the test in a column.

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SCONF C C

SCONF stores the confidence interval for the mean in two columns.

Graphs

Displays a graph for each column listed with ONEZ. Each graph displays the sample mean and a K% confidence interval

for the mean. No graph is displayed for the summarized data.

GHISTOGRAM

Displays a histogram.

GINDPLOT

Displays an individual value plot.

GBOXPLOT

Displays a boxplot.

ONET: Session command for performing a 1-sample

t-test

ONET C...C

ONET K K K

Performs a t-test using raw or summarized data. The population standard deviation for the test is estimated form

the columns listed with ONET. A test value is specified with the TEST subcommand. ONET performs a two-sided

test unless you use the ALTERNATIVE subcommand to specify a one-sided test. If you know the population

standard deviation or deviations, use ONEZ on page 147 instead.

ONET C...C performs a t-test for each of the columns in C...C.

ONET K K K performs a t-test for the summarized data using sample size, sample mean, and population standard

deviation specified in K, K, and K, respectively.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default

value of K is 95.

ALTERNATIVE K

Enter K to specify the direction of the alternative hypothesis.

Format of the alternative hypothesisValue of K

Less than–1

Not equal to0 (default)

Greater than1

TEST K

Tests the null hypothesis using the hypothesized mean specified in K.

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SPVALUE C

SPVALUE stores the p-value of the test in a column.

SCONF C C

SCONF stores the confidence interval for the mean in two columns.

Graphs

Displays a graph for each column listed with ONET. Each graph displays the sample mean and a K% confidence interval

for the mean. No graph is displayed for the summarized data.

GHISTOGRAM

Displays a histogram.

GINDPLOT

Displays an individual value plot.

GBOXPLOT

Displays a boxplot.

TWOT: Session command for performing a 2-sample

t-test when samples are in one column

TWOT C C

TWOT K K K K K K

TWOT performs a two-sample t-test and confidence interval when the samples and subscripts are in separate

columns or when you have summarized data. Use TWOSAMPLE on page 150 when the samples are in different

columns.

You can specify a confidence level to change the default level for the confidence interval produced by TWOT. The

default level is 95%. K can be any number between 1 and 100. For example, if you enter the command TWOT 90

C1 C2, Minitab calculates a 90% confidence interval.

TWOT C C performs a 2-sample t-test when the samples are in one column and subscripts are in another column.

TWOT K K K K K K performs a 2-sample t-test using summarized data with sample size, mean, and standard

deviation listed for each sample.

ALTERNATIVE K

Specifies a one-sided test. K = –1 gives H1: μ < K, and K = +1 gives H1: μ > K1.

TEST K

Specifies the null hypothesis value K.

POOLED

Uses a pooled procedure to estimate σ. This procedure assumes the two populations have equal variances.

The POOLED procedure is slightly more powerful than the method that does not assume equal variances,

but can be seriously in error if the variances are not equal. Thus, the POOLED subcommand should not be

used in most cases.

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GINDPLOT

The graph for GINDPLOT contains two individual value plots, one for each sample. The individual value plots

displays the sample mean and a K% confidence interval for each sample.

GBOXPLOT

The graph for GBOXPLOT contains two boxplots, one for each sample. The boxplots display the sample mean

and a K% confidence interval for each sample.

SPVALUE C

SPVALUE stores the p-value of the test in a column.

SCONF C C

SCONF stores the confidence interval for the mean in two columns.

TWOSAMPLE: Session command for performing a

2-sample t-test when the samples are in different

columns

TWOSAMPLE

TWOSAMPLE performs a 2-sample t-test and confidence interval when the samples are in different columns. Use

TWOT on page 149 when samples and subscripts are in separate columns or when you have summarized data.

You can specify a confidence level to change the default level for the confidence interval produced by TWOSAMPLE.

The default level is 95%. K can be any number between 1 and 100. For example, if you enter the command

TWOSAMPLE 90 C1 C2, then a 90% confidence interval is calculated.

ALTERNATIVE K

Specifies a one-sided test. K = –1 gives H1: μ < K, and K = +1 gives H1: μ > K1.

TEST K

Specifies the null hypothesis value K.

POOLED

Uses a pooled procedure to estimate σ. This procedure assumes the two populations have equal variances.

The POOLED procedure is slightly more powerful than the method that does not assume equal variances,

but can be seriously in error if the variances are not equal. Thus, the POOLED subcommand should not be

used in most cases.

GINDPLOT

The graph for GINDPLOT contains two individual value plots, one for each sample. The individual value plots

displays the sample mean and a K% confidence interval for each sample.

GBOXPLOT

The graph for GBOXPLOT contains two boxplots, one for each sample. The boxplots display the sample mean

and a K% confidence interval for each sample.

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SPVALUE C

SPVALUE stores the p-value of the test in a column.

SCONF C C

SCONF stores the confidence interval for the mean in two columns.

PAIR: Session command for performing a paired

t-test

PAIR C C

PAIR K K K

Performs a paired t-test. This test is appropriate for testing the mean difference between paired observations

when the paired differences follow a normal distribution.

Use to calculate a confidence interval and perform a hypothesis test of the mean difference between paired

observations in the population. A paired t-procedure matches responses that are dependent or related in a pairwise

manner. This matching allows you to account for variability between the pairs usually resulting in a smaller error

term, thus increasing the sensitivity of the hypothesis test or confidence interval.

Typical examples of paired data include measurements on twins or before-and-after measurements. For a paired

t-test, the hypotheses are as follows:

: μ

= μ

versus H

: μ

where μ

is the population mean of the differences and μ

is the hypothesized mean of the differences.

When the samples are drawn independently from two populations, use TWOSAMPLE on page 150 or TWOT on

page 149.

PAIR C C performs a paired t-test when the data are in columns.

PAIR K K K performs a paired t-test using summarized data when K = sample size, K = mean difference, and K =

standard deviation difference.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default

value of K is 95.

ALTERNATIVE K

Specifies a one-sided test. K = –1 gives H

: μ

< μ

, and K = +1 gives H

: μ

> μ

TEST K

Conducts a test of the null hypothesis μ = K.

SPVALUE C

SPVALUE stores the p-value of the test in a column.

SCONF C C

SCONF stores the confidence interval for the mean in two columns.

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Graphs

Each of the following graphs displays the sample mean, a K% confidence interval for the mean, and the value under

GHISTOGRAM

Displays a histogram of the paired differences.

GINDPLOT

Displays an individual value plot of the paired differences.

GBOXPLOT

Displays a boxplot of the paired differences.

PONE: Session command for performing a

hypothesis test of the proportion

PONE C...C

PONE K K...K

Performs a test of one binomial proportion.

Use PONE to compute a confidence interval and perform a hypothesis test of the proportion. For example, an

automotive parts manufacturer claims that his spark plugs are less than 2% defective. You could take a random

sample of spark plugs and determine whether or not the actual proportion defective is consistent with the claim.

For a two-tailed test of a proportion:

: p = p

versus H

: p p

where p is the population proportion and p

is the hypothesized value.

To compare two proportions, use PTWO on page 154.

PONE C...C performs an analysis of one proportion on samples in columns.

PONE K K...K performs an analysis of one proportion on summarized data where K = the number of trials and

subsequent Ks = the number of events.

EVENT K

Specifies the event of interest in a column of data. Use a value from the column for K. Enclose text values in

quotation marks. For example, to assess the proportion of "Failure" in a column, enter EVENT "Failure".

TEST K

Specifies the null hypothesis value K.

Options

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

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ALTERNATIVE K

Specifies the alternative hypothesis and the type of confidence interval. K = –1 gives H1: p < p

and an upper

bound for p. K = +1 gives H1: p > p

and a lower bound for p. The default of K = 0 gives H1: p ≠ p

and a two-sided

interval for p.

ABLAKER

When the argument for ALTERNATIVE is 0, specifies to use the adjusted Blaker's exact method for calculating the

two-sided hypothesis test and a two-sided confidence interval.

When the argument for the ALTERNATIVE subcommand is -1 or 1, specifies to use the Clopper-Pearson method

to calculate one-sided hypothesis tests and confidence intervals.

CTOLERANCE K

Specifies the convergence criterion for the calculation of the p-value and confidence interval for the adjusted

Blaker's exact method. K is a positive, real number. The default value is 10

-10

ITERATION K

Specifies the maximum number of iterations for the calculation of the p-value and confidence interval for

the adjusted Blaker's exact method. K is a non-negative integer. The default value is 100.

WILSON

Specifies to use the classical Wilson-score approximate procedure for calculating the hypothesis test and confidence

interval. The method is also known as the score method. To use the method with a continuity correction, issue

the CCORRRECTION sub-subcommand.

CCORRRECTION

Specifies to use the Wilson-score approximate method with a continuity correction. Without a continuity

correction, the method is liberal for small to moderate sample sizes. The continuity correction makes the

actual confidence level and the actual alpha value at least the levels that the analysis specifies.

ACOULL

Specifies to use the Agresti-Coull approximate procedure for calculating the hypothesis test and confidence

interval.

CPEARSON

Specifies to use the Clopper-Pearson exact method for calculating the hypothesis test and confidence interval.

USEZ

Specifies to use the normal approximation to the binomial distribution for calculating the hypothesis test and

confidence interval.

Storage

SPVALUE C

SPVALUE stores the p-value of the test in a column.

SCONF C (C)

For the default analysis or when the argument for ALTERNATIVE is 0, stores the lower side of the confidence

interval in the first column and the upper side of the confidence interval in the second column.

When the argument for ALTERNATIVE is -1 or 1, stores the confidence limit in one column.

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PTWO: Session command for performing a

hypothesis test of the difference between two

proportions

PTWO C C

PTWO K K K K

Performs a test of two binomial proportions.

Use PTWO to compute a confidence interval and perform a hypothesis test of the difference between two

proportions. For example, suppose you wanted to know whether the proportion of consumers who return a survey

could be increased by providing an incentive such as a product sample. You might include the product sample

with half of your mailings and see if you have more responses from the group that received the sample than from

those who did not. For a two-tailed test of two proportions:

: p

= p

versus H

: p

where p

and p

are the proportions of success in populations 1 and 2, respectively.

To test one proportion, use PONE on page 152.

You can input either raw or summarized data.

Raw data can be entered in two ways: stacked and unstacked.

•

Enter both samples in a single column (stacked) with a group column to identify the population. Columns may

be numeric, text, or date/time. Successes and failures are determined by numeric or alphabetical order. Minitab

defines the lowest value as the failure; the highest value as the success. For example:

◦

For the numeric column entries of "5" and "10", observations of 5 are considered failures; observations of

10 are considered successes.

◦

For the text column entries of "agree" and "disagree," observations of agree are considered failures;

observations of disagree are considered successes. If the data entries are "yes" and "no", observations of

no are considered failures; observations of yes are considered successes.

•

Enter each sample (unstacked) in separate numeric or text columns. Both columns must be the same

type-numeric or text. Successes and failures are defined as above for stacked data.

You can reverse the definition of success and failure in a text column by applying a value order. For more

information, go to VORDER on page 137.

The sample sizes do not need to be equal. Minitab automatically omits missing data from the calculations.

For raw data, enter the number of trials and the number of successes for each sample on the main command line.

Enter four integers: the number of trials and the number of successes in the first sample followed by the number

of trials and the number of successes in the second sample.

PTWO C C performs a test of two proportions on samples in columns.

PTWO K K K K performs a test of two proportions on summarized data where K = the number of trials and K =

the number of events for the first proportion, and K = the number of trials and K = the number of events for the

second proportion.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default

value of K is 95.

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Minitab Statistical Software Basic Statistics

ALTERNATIVE K

Specify a one-sided test. K = –1 gives H1: p < K, and K = +1 gives H1: p > K1.

TEST K

Specifies the null hypothesis value K.

STACKED

Specifies that the data have been entered in a stacked format: the raw data is in one column and the subscripts

or group column to identify the population are in a second column.

POOLED

Specifies to use a pooled estimate of p to calculate the test statistic.

SPVALUE C

Stores the p-value of the test in a column.

SFISHER C

Stores the p-value for the Fisher's exact test in a column.

SCONFC C

Stores the confidence interval for the mean in two columns.

ONERATE: Session command for performing a

1-sample Poisson rate test

ONERATE C...C

ONERATE K K...K

Performs a hypothesis test and calculates a confidence interval for the population Poisson rate for each sample

you input. You can enter data in raw, summarized, or frequency format. Specify a hypothesized test value with

the TEST subcommand. Minitab performs a two-sided test unless you use the ALTE subcommand to specify a

one-sided test. Specify the length of the observation space with the LENGTH subcommand to analyze the mean

number of occurrences in addition to the occurrence rate.

ONERATE C...C calculates a confidence interval for the population Poisson rate for each column of raw sample

data.

ONERATE K K...K calculates a confidence interval for the population Poisson rate for summarized data. The first

K equals the sample size; subsequent values of K correspond to the number of events in each summarized sample.

You cannot use this command to analyze samples with different sample sizes.

FREQ C...C

If your data exists in frequency format, use this subcommand to specify the frequency columns. Enter them

in the same order in which you enter the columns of unique observations.

LENGTH K [K]...[K]

Specify the length of the observation space for each sample to obtain confidence intervals of mean number

occurrences in addition to the rate of occurrences. By default, length equals 1, and the occurrence rate equals

the mean number of occurrences.

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Minitab Statistical Software Basic Statistics

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default

value of K is 95.

ALTERNATIVE K

Enter K to specify the direction of the alternative hypothesis.

Format of the alternative hypothesisValue of K

Less than–1

Not equal to0 (default)

Greater than1

TEST K

Performs a hypothesis test of population Poisson rate. Enter the hypothesized value of the population Poisson

rate for K.

USEZ

Specifies the normal approximation method for calculating the hypothesis test and confidence interval. By

default, Minitab uses the exact method instead of the normal approximation method. This subcommand

accepts no arguments.

TWORATE: Session command for performing a

2-sample Poisson rate test

TWORATE C C

TWORATE K K K K

Use TWORATE C C to perform a hypothesis test and calculates a confidence interval for the difference between

the Poisson rates of two populations. For unstacked data, the arguments correspond to the two columns of data.

For stacked data, the first C corresponds to the column of stacked sample data, and the second C corresponds

to the column of subscripts.

Use TWORATE K K K K with summarized data to perform a hypothesis test and calculate a confidence interval for

the difference between the Poisson rates of two populations. The first K equals the size of the first sample; the

second K equals the number of occurrences in the first sample; the third K equals the size of the second sample;

the fourth K equals the number of occurrences in the second sample.

LENGTH K [K]

Specify the length of observation for each sample to analyze both the mean number of occurrences and the

occurrence rate. If you enter one K, this value becomes the length for both samples. If you enter two values

for K, the first becomes the length for the first sample, and the second K becomes the length of the second

sample.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default

value of K is 95.

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Minitab Statistical Software Basic Statistics

ALTERNATIVE K

Enter K to specify the direction of the alternative hypothesis.

Format of the alternative hypothesisValue of K

Less than–1

Not equal to0 (default)

Greater than1

TEST K

Specifies the hypothesized value for the difference between the two population Poisson rates. The default

value is 0.

STACKED

Indicates that the data for both samples are stacked in a single column. This subcommand accepts no

arguments.

FREQ C [C]

Use this subcommand if your data exists in frequency format. For stacked data, enter only one column of

frequencies. For unstacked data, enter two columns of frequency data in the same order in which you enter

the columns of unique observations.

POOLED

Uses a pooled estimate of the occurrence rate for both populations. The argument for the TEST subcommand

must equal zero to use this option. By default, Minitab estimates the rate of each population separately by

using the observed sample rates. This subcommand accepts no arguments.

ONEV: Session command for performing a 1 variance

test

ONEV C...C

ONEV K K

For each sample you input, this command performs a one variance hypothesis test and produces a confidence

interval for the population variance. A test value is specified with the STEST or VTEST subcommands. Minitab

performs a two-sided test unless you use the ALTERNATIVE subcommand to specify a one-sided test. If you prefer

to work in terms of standard deviation instead of variance, use the STDEV subcommand.

ONEV C...C calculates a confidence interval for the population variance for each column of raw data. Output

includes results of the chi-square method (for normal distributions) and the Bonett method (for any continuous

distribution).

ONEV K K calculates a confidence interval for the population variance with summarized data. The first K equals

the sample size; the second K equals the sample variance. Output only includes results of the chi-square method

(for normal distributions).

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default

value of K is 95.

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Minitab Statistical Software Basic Statistics

ALTERNATIVE K

Enter K to specify the direction of the alternative hypothesis.

Format of the alternative hypothesisValue of K

Less than–1

Not equal to0 (default)

Greater than1

STEST K

STEST performs a hypothesis test of the population standard deviation.

STEST and VTEST are mutually exclusive.

VTEST K

VTEST performs a hypothesis test of the population variance. K specifies the hypothesized value of the

population and must be a positive number.

STEST and VTEST are mutually exclusive.

STDEV

This subcommand indicates that input values refer to standard deviation instead of variance, where applicable.

Furthermore, this subcommand causes Minitab to produce output in terms of standard deviation instead of

variance. By default, Minitab interprets input and produces output in terms of variance. This subcommand

accepts no arguments.

TWOVARIANCES: Session command for determining

whether the variances or standard deviations of two

groups differ

TWOVARIANCES performs hypothesis tests and computes confidence intervals for the ratios between two populations'

variances and standard deviations. You can use this command when samples and subscripts are in separate columns,

when each sample is in a single column, or when you have summarized data.

TWOVARIANCES C C

Performs two variances test when sample data are in different columns.

TWOVARIANCES C C

Performs two variances test when all sample data are in one column C and subscripts are in a second column C.

Use STACKED with this option.

STACKED

Specifies that data are stacked.

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Minitab Statistical Software Basic Statistics

TWOVARIANCES K K K K

Performs two variances test using summarized data. Specify the sample size and variance for Sample 1 and the

sample size and variance for Sample 2. If you specify standard deviations instead of variances, use the STDEV

subcommand.

Options

STDEV

Indicates that the input summary values refer to standard deviations instead of variances.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

ALTERNATIVE K

Enter K to specify the direction of the alternative hypothesis.

Format of the alternative hypothesisValue of K

Less than–1

Not equal to0 (default)

Greater than1

STEST K

STEST specifies the hypothesized value K for the ratio of two standard deviations. K must be a positive number.

The default is 1, which tests for equal standard deviations. STEST and VTEST are mutually exclusive.

VTEST K

VTEST specifies the hypothesized value K for the ratio of two variances. K must be a positive number. The default

is 1, which tests for equal variances. STEST and VTEST are mutually exclusive.

SNAMES K K

Specifies names for the samples when you enter summarized data. The default sample names are "Sample 1" and

"Sample 2".

USEF

Specifies to use the F-test method instead of Bonett's method and Levene's method. The F-test is accurate only

for normally distributed data. Any departure from normality can cause the F-test to yield inaccurate results.

However, if the data conform to the normal distribution, then the F-test is typically more powerful than either

Bonett's test or Levene's test.

Graphs

GINTERVAL

GINTERVAL displays a graphical summary of the results including the following:

•

Confidence intervals for the ratio of the standard deviations or variances

•

P-values for the hypothesis tests

•

Boxplots of each sample

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Minitab Statistical Software Basic Statistics

GHISTOGRAM

GHISTOGRAM displays a graph containing a histogram for each sample.

GINDPLOT

GINDPLOT displays a graph containing an individual value plot for each sample.

Results

NODEFAULT

Specifies that none of the following results are displayed.

TMETHOD

TMETHOD displays the method table which includes the null hypothesis, the alternative hypothesis, and the

significance level (denoted by α or alpha).

TSTATISTICS

TSTATISTICS displays the statistic table which includes the standard deviation of each sample, the variance of

each sample, and the confidence interval for the standard deviation or the variance of each sample. The ratio of

the standard deviations and the ratio of the variances are also displayed.

TCONFIDENCE

TCONFIDENCE displays the confidence interval table which includes confidence intervals for the ratio of the

standard deviations and the ratio of the variances.

TTEST

TTEST displays the test table which includes the degrees of freedom, the test statistics, and the p-values. When

the sample sizes are unequal, the test statistic for Bonett's method is undefined. However, the p-value can be

calculated by inverting the confidence interval procedure.

CORRELATION: Session command for measuring

the strength and direction of the association

between two variables

CORRELATION C...C

Calculates the Pearson product moment correlation or the Spearman rank-order correlation for every pair of

samples in C...C.

Options

Minitab calculates the Pearson product moment correlation or the Spearman rank-order correlation and the associated

confidence intervals and p-values.

PEARSON

Calculates the Pearson (product-moment) correlation. Use the Pearson correlation coefficient to examine the

strength and direction of the linear relationship between two continuous variables.

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Minitab Statistical Software Basic Statistics

For each pair of samples, Minitab calculates the p-value for the hypothesis test of zero-correlation and the

confidence interval for the Pearson correlation. The confidence intervals are based on the Fisher's Z transformation.

SPEARMAN

Calculates the Spearman rank-order correlation (also called Spearman's rho). The Spearman correlation evaluates

the monotonic relationship between two continuous or ordinal variables. In a monotonic relationship, the variables

tend to change together, but not necessarily at a constant rate. The Spearman correlation coefficient is based on

the ranked values for each variable rather than the raw data.

Spearman correlation is often used to evaluate relationships involving ordinal variables. For example, you might

use a Spearman correlation to evaluate whether the order in which employees complete a test exercise is related

to the number of months they have been employed.

For each pair of samples, Minitab calculates the p-value for the hypothesis test of zero-correlation and the

confidence interval for the Spearman correlation. The confidence intervals are based on the Fisher's Z transformation

of the Spearman correlation and Bonett and Wright adjustments.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

Results

NODEFAULT

Suppresses the display of default tables and graphs.

TMETHOD

Displays the Method table which includes the correlation method. When the matrix plot includes p-values or the

output includes the table of pairwise correlations, the Method table also includes the hypotheses and the

significance level.

TCORRELATION

Displays the correlation matrix.

TPCORRELATION M

Displays the table of pairwise correlations. The table includes pairwise sample sizes, the correlation estimates, the

confidence intervals, and the p-values.

Graphs

GMPLOT

Displays a matrix of scatterplots for all pairs. By default, the matrix plot includes correlation values and the matrix

plot is the lower left triangle of the matrix so that each pair of variables is on one plot. There are two groups of

optional subcommands used to control the statistics and the display of the plot.

Use one of the following optional subcommands to change the display statistics:

Displays the correlation values on the plots.

RPVALUES

Displays the correlation values and the p-values for the test of zero-correlation on the plots.

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Minitab Statistical Software Basic Statistics

RCIS

Displays the correlation values and the confidence intervals on the plots.

NOSTATS

Suppresses the display of statistics on the plots.

Use one of the following optional subcommands to specify how to display the matrix plot:

Displays the lower left triangle portion of the matrix plot.

Displays the upper right triangle portion of the matrix plot.

FULL

Displays the entire matrix plot (both the lower left triangle and the upper right triangle) of the matrix plot.

Storage

SCORRELATION M

Stores the correlation matrix.

SPVALUES M

Stores a matrix that contains the p-values for the hypothesis test of zero-correlation.

SCIS M M

Stores two matrices that contain the lower bounds and the upper bounds of the confidence intervals for the

pairwise correlations. The first matrix stores the lower bounds and the second matrix stores the upper bound.

COVARIANCE: Session command for calculating the

covariance between pairs of columns

COVARIANCE C...C [M]

Calculates the covariance between each pair of columns. You can display the result or you can store the covariance

matrix.

When you list two columns on the command line, Minitab calculates the covariance for the pair. When you list

more than two columns, Minitab calculates the covariance for every pair of columns, and displays the lower triangle

of the resulting covariance matrix (in blocks if there is insufficient room to fit across a page). Minitab does not

display the covariance matrix when the matrix is stored in M.

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Minitab Statistical Software Basic Statistics

NORMTEST: Session command for performing a

normality test

NORMTEST C

Generates a normal probability plot.

Normal plots use the values in the input column as x-values. The grid on the graph resembles the grids found on

normal probability paper. The horizontal axis is a linear scale. The line forms an estimate of the cumulative

distribution function for the population from which data are drawn.

By default, an Anderson-Darling test for normality is performed and the numerical results are displayed with the

graph. You can also use a Ryan-Joiner test (similar to a Shapiro-Wilk test) or a Kolmogorov-Smirnov test.

PTILES C

PTILES K...K

Specifies a set of reference percents. The values must be between 0 and 100 when percents are used as the

y-scale type or 0 to 1 when probability is the y-scale type. Minitab marks each percent in the column with a

horizontal reference line on the plot, and marks each line with the percent value. Minitab draws a vertical

reference line where the horizontal reference line intersects the line fit to the data, and marks this line with

the estimated data value.

DVALUE

Use DVALUE to show the percents at the reference x-scale positions specified in PTILES.

PERCENT

Specifies a percent y-scale.

PROBABILITY

Specifies a probability y-scale.

SCORES

Specifies a percentile y-scale.

TITLE "title"

Specifies a title for the graph. If you do not specify a title, Minitab uses a default title.

WTITLE "title"

You can use WTITLE as a subcommand with LAYOUT and all graphs. The title that you specify becomes the

command title of the resulting graph.

SPVALUE C

SPVALUE stores the p-value of the test in a column.

GSAVE "file_name"

GSAVE K

Saves the graph in a file.

The default file name is Minitab.PNG. You can specify a custom file name in double quotation marks

("file_name"), or as a stored text constant (K). You can also use any of the following subcommands to save

the graph in a different graphics format.

Some graph commands—for example, HISTOGRAM C1 C2 C3—generate more than one graph. If you include

the GSAVE subcommand with such a command, Minitab saves multiple files. Minitab gives each file a different

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Minitab Statistical Software Basic Statistics

file name. Minitab uses the first five characters of the name you specify, then appends a number (001, 002,

and so on), for up to 300 files.

JPEG

JPEG color

PNGB

PNG grayscale

PNGC

PNG color

TIFB

TIF grayscale

TIF

TIF color

BMPB

BMP grayscale

BMPC

BMP color

GIF

EMF

RESOLUTION K

Saves the graph at a resolution of K dots per inch.

Goodness-of-fit tests

There are 3 types of goodness-of-fit test: a chi-square based test, an ECDF based test, and a correlation based test.

By default, Minitab uses the Anderson-Darling test, which is an ECDF based test.

When your α-value is greater than the p-value displayed with the graph, you reject the hypothesis of normality. The

α-value (also called significance level), is the probability that you will reject the hypothesis of normality when the

hypothesis is true.

For example, if you are using an α-value of 0.10 and the p-value is 0.07, then you reject the hypothesis of normality

at the 0.10 level.

RJTEST

Use RJTEST to perform a Ryan-Joiner test, which is a correlation based test.

KSTEST

Use KSTEST to perform a Kolmogorov-Smirnov test, which is a chi-square based test.

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Minitab Statistical Software Basic Statistics

OUTLIER: Session command for performing an

outlier test

OUTLIER C...C

Use to identify a single outlier in a sample. The hypotheses are as follows:

•

(the null hypothesis): All values in the sample are from the same, normally distributed population.

•

(the alternative hypothesis): One of the values in the sample is not from the same, normally distributed

population.

You should not use Minitab's outlier tests more than once on the same sample. If you remove an outlier from

your sample and then retest, you risk removing values that are not actually outliers.

BY C...C

Lists the columns that contain the grouping variables (such as a column named Temp that contains the values

Low, Medium, and High). Columns listed with BY may contain numeric, text, or date time data. When you

include the BY subcommand, OUTLIER conducts a separate analysis for each group listed in the BY column

or columns.

Options

GRUBBS

GRUBBS specifies to use Grubbs' test, which is the default test.

The arguments K K specify the specific ratio to test. For example, to perform the standard Dixon's Q test, which

corresponds to a Dixon ratio of r10, the arguments are 1 and 0. The first argument can either be 1 or 2 and the

second argument can be 0, 1, or 2.

DIXON K K

DIXON specifies to use one of the six versions of Dixon's outlier test instead of Grubbs' test.

The arguments K K specify the specific ratio to test. For example, to perform the standard Dixon's Q test, which

corresponds to a Dixon ratio of r10, the arguments are 1 and 0. The first argument can either be 1 or 2 and the

second argument can be 0, 1, or 2.

ALPHA K

Specifies the significance level (denoted by α or alpha) for the test as K. The default is 0.05.

ALTERNATIVE K

Enter K to specify the direction of the alternative hypothesis.

Format of the alternative hypothesisValue of K

Less than–1

Not equal to0 (default)

Greater than1

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Minitab Statistical Software Basic Statistics

Graphs

GOUTLIERPLOT

Displays the outlier plot with a summary of the results, including an individual value plot of the data which

highlights the outlier if one is identified. The outlier plot also includes summary statistics, the test statistic, and

the p-value for the test statistic.

Results

Minitab displays all output tables by default; you do not have to enter the subcommands.

NODEFAULT

If you enter NODEFAULT, then each table is only displayed if you enter TMETHOD, TTEST, or TOUTLIER.

TMETHOD

TMETHOD displays the method table which includes the null hypothesis, the alternative hypothesis, and the

significance level significance level (denoted by α or alpha).

TTEST

TTEST displays the test table which includes the specific test performed, summary statistics for the sample, and

the p-value for the hypothesis test.

TOUTLIER

TOUTLIER displays the value of the outlier if one is identified, and the row in the worksheet that contains the

outlier.

Storage

SINDICATOR C...C

Stores an indicator column for each variable. A one (1) is stored in the row that contains the outlier. A zero (0) is

stored in each of the remaining rows.

PGOODNESS: Session command for performing a

chi-square goodness-of-fit test for Poisson

distribution

PGOODNESS C...C

Performs a chi-square goodness-of-fit test for Poisson distribution for each data set. Each C corresponds to one

data set containing nonnegative integers. If the column contains missing data, that row is excluded from all

computations.

FREQUENCY C...C

Specifies the frequency column, one for each data set. The length of each column must match the length of

the corresponding data set.

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Minitab Statistical Software Basic Statistics

MEAN C

MEAN K...K

Specifies the mean of the Poisson distribution for each data set as K...K or in C. If you specify only one value

for the mean, then this value applies to all data set.

Graphs

GBAR

Displays a bar chart of the observed and expected values.

GCHISQ

Displays a bar chart of each category's contribution to the chi-square value.

PARETO

Use PARETO with GCHISQ to display a bar chart that orders each category's contribution to the chi-square

value from largest to smallest.

Results

RTABLE

Displays a table of the observed values and the expected values and the test results.

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Regression

INDICATOR: Session command for creating indicator

variables

INDICATOR C C...C

Creates indicator variables (also called dummy variables) that you can use in a regression analysis. If you use

REGRESS on page 169, you do not need to create indicator variables.

The smallest number in C2 is 2 and the largest is 6. INDICATOR creates one indicator variable for each unique

value.

•

C11 is the indicator variable for the value 2. C11 contains a 1 in every row where C2 contains a 2, and 0

otherwise.

•

C12 is the indicator variable for the value 3. C12 contains a 1 in every row where C2 contains a 3, and 0

otherwise.

•

C13 is the indicator variable for 5. C13 contains a 1 in every row where C2 contains 5, and 0 otherwise.

•

C14 is the indicator variable for 6. C13 contains a 1 in every row where C2 contains 6, and 0 otherwise.

If C2 contains an * (missing data code), then all indicator variables are also set to *.

The number of storage columns must be equal to the number of distinct values (not including *) in the input

column. Up to 100 storage columns are allowed on INDICATOR.

The following command language is an example of using INDICATOR.

INDICATOR C2, C11-C14

Before using INDICATOR

After using INDICATOR

C14C13C12C11...C1

0001...2

0100...5

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Minitab Statistical Software Regression

C14C13C12C11...C1

0010...3

1000...6

0100...5

****...*

REGRESS: Session command for performing a

regression analysis

REGRESS

Performs simple, polynomial, and multiple regression using the least squares method.

REGRESS uses least squares to fit a model to one or more continuous or categorical predictors. Optionally, you

can:

•

Perform stepwise regression

•

Fit the model without an intercept

•

Perform weighted regression

•

Use a test data set or cross-validation to validate the model

•

Store the residuals, fitted values, and many other diagnostics for additional analysis

•

Generate point estimates, and prediction and confidence intervals for predicted values

•

Transform non-normal data using the Box-Cox transformation

•

Generate several plots for residual analysis. For more information, go to Residual analysis and regression

diagnostics on page 1178.

Options

Specifies information about the variables and terms to include in the model.

RESPONSE C

Specifies the column that contains the response variable. The column must be numeric or date/time.

CONTINUOUS C...C

Specifies the continuous predictors if you have any. The column(s) must be numeric or date/time and must match

the length of the response column.

CATEGORICAL C...C

Specifies the categorical predictors if you have any. The column(s) can be numeric, text, or date/time and must

match the length of the response column.

TERMS termlist

Specifies the model terms. Terms must be legal cross-terms. Only continuous predictors may be repeated. Nested

terms are not allowed. The model can be nonhierarchical.

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WEIGHT C

Performs a weighted regression. An n x n matrix W is formed with the column of weights as its diagonal and zeros

elsewhere. The regression coefficients are estimated by:

(X' W X)

−1

(X' W Y)

This is equivalent to minimizing the weighted SS Error,

Σ w

(Y – )

where w i is the weight in row i.

CONSTANT

When you use CONSTANT, Minitab includes the β

term (the intercept) in the equation. Thus, Minitab fits the

model:

Y = β

+ β

+...+ β

+ e

NOCONSTANT

When you use NOCONSTANT, Minitab omits the β

term (the intercept) from the equation. Thus, Minitab fits the

model:

Y = β

+ β

+...+ β

+ e

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

ITYPE K

Specifies the type of confidence interval.

Type of confidence intervalValue of K

Lower bound–1

Two-sided0 (default)

Upper bound1

TOLER K

Specifies the tolerance level for collinearity and constant check. By default, K = 4 * 2.22e–012.

SSQUARES

Specifies sequential sum of squares for tests in the ANOVA table. The default is the adjusted sums of squares.

EFFECT

Specifies the effect coding (−1, 0, +1) scheme for categorical predictors.

If you do not specify either EFFECT or BINARY, Minitab uses the preference set in File > Options > Linear Models

> Coding of Predictors.

BINARY

Specifies the binary coding (1, 0) scheme for categorical predictors.

If you do not specify either EFFECT or BINARY, Minitab uses the preference set in File > Options > Linear Models

> Coding of Predictors.

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REFERENCE C K ... C K

Changes the default coding for the categorical predictor columns. To change the default reference factor level,

specify the factor column followed by the reference level. (You must enclose text and date/time levels in double

quotation marks.) You can assign a reference level only when you use the binary coding (1, 0) scheme.

Standardizing the continuous predictor

Use this set of subcommands to standardize the continuous predictors in your model. You can use SCALE and LOCATION

in conjunction with each other. LEVELS is mutually exclusive with the LOCATION and SCALE subcommands.

If you do not specify LOCATION, SCALE, LEVELS, or UNSTANDARDIZED, Minitab uses the preferences set in File >

Options > Linear Models > Coding of Predictors.

LOCATION [K...K]

Specifies that the analysis is to be performed on coded continuous predictors by subtracting a constant from

each predictor. If you do not specify any arguments, the mean of each predictor column is subtracted. K specifies

to subtract a constant. If you specify arguments, the number of arguments must match the number of continuous

predictors.

SCALE [K...K]

Specifies that the analysis is to be performed on coded continuous predictors by dividing each predictor by a

constant. If you do not specify any arguments, each predictor column is divided by the standard deviation. K

specifies to divide by a constant. If you specify arguments, the number of arguments must match the number of

continuous predictors.

LEVELS [K...K]

Specifies that the analysis is to be performed on coded continuous predictors by DOE-type coding for the specified

low and high levels K K&K K. The number of arguments must be twice the number of continuous predictors.

UNSTANDARDIZED

Specifies the analysis is to be performed on the original predictors.

Box-Cox

BOXCOX [K]

Performs a Box-Cox transformation with a specified lambda. K is the value of lambda and must be between −5

and +5. If you do not specify K, then Minitab will find the optimal lambda. By default, Minitab rounds the optimal

value.

Minitab cannot calculate the optimal lambda for stepwise regression. Consequently, you must specify a lambda

value for BOXCOX if you use STEPWISE, FORWARD, or BACKWARD.

Stepwise

FINFORMATION, STEPWISE, FORWARD, and BACKWARD perform a stepwise regression procedure to fit the model. No

arguments are needed for these subcommands.

FINFORMATION

Specifies a stepwise model selection procedure that uses forward information criteria selection. Use AICCORRECTED

or BICRITERION to specify which information criterion to use to select the final model. If you do not specify a

criterion, Minitab uses AICCORRECTED.

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The forward information criteria procedure adds the term with the lowest p-value to the model at each step. If

you do not include subcommands about hierarchy, FINFORMATION adds 1 term at a step and maintains model

hierarchy, the equivalent of the following:

HIERARCHICAL; ALLTERMS; ALWAYS; SINGLE.

Minitab calculates the information criteria for each step.

In most cases, the procedure continues until one of the following conditions occurs:

•

The procedure does not find an improvement of the criterion for 8 consecutive steps.

•

The procedure fits the full model.

•

The procedure fits a model that leaves 1 degree of freedom for error.

If you specify settings for the procedure that require a hierarchical model at each step and allow only one term

to enter at a time, then the procedure continues until it either fits the full model or fits a model that leaves 1

degree of freedom for error. Minitab displays the results of the analysis for the model with the minimum value

of the selected information criterion, either the corrected Akaike's Information Criterion (AICc) or the Bayesian

Information Criterion (BIC).

AICCORRECTED

Specifies the use of the corrected Akaike's Information Criterion (AICc) to select the final model.

BICRITERION

Specifies the use of the Bayesian Information Criterion (BIC) to select the final model.

FVALIDATION

Specifies the use of forward selection with the validation R

as the criterion for the selection of the final model.

When you use FVALIDATION, you must also use TEST or CVTEST. When you use TEST, the procedure is similar

to forward selection for other methods. At the end of each step, Minitab Statistical Software calculates the test

statistic. At the end of the forward selection procedure, the model with the greatest test R

value is the final

model.

When you use CVTEST, the procedure repeats forward selection on each fold. The procedure evaluates all the

folds at each step and identifies the step with the best overall k-fold R

value. The last part of the procedure is to

perform forward selection on the full dataset, stopping at the best step from the selections on the folds.

FVALIDATION stops under the same conditions as FINFORMATION. The ATEND sub-subcommand that adds

terms to the model to make it hierarchical at the end is not compatible with FVALIDATION.

GRSQUARE

When you use GRSQUARE after FVALIDATION, displays a graph of the R

statistic and the validation R

statistic

for each step in the model selection procedure.

STEPWISE

Specifies a stepwise model selection procedure that uses both forward selection and backward elimination. If you

do not include subcommands about hierarchy, STEPWISE and FORWARD add 1 term at a step and maintain model

hierarchy, the equivalent of the following:

HIERARCHICAL; ALLTERMS; ALWAYS; SINGLE.

FORWARD

Specifies a stepwise model selection procedure that uses forward selection. If you do not include subcommands

about hierarchy, STEPWISE and FORWARD add 1 term at a step and maintain model hierarchy, the equivalent of

the following:

HIERARCHICAL; ALLTERMS; ALWAYS; SINGLE.

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BACKWARD

Specifies a stepwise model selection procedure that uses backward elimination. Removes a single term at each

step and maintains a hierarchical model, the equivalent of the following:

HIERARCHICAL; ALLTERMS; ALWAYS.

AENTER K

Specifies the alpha level at which a term is entered into the model. The default is 0.15 for STEPWISE and 0.25

for FORWARD.

AREMOVE K

Specifies the alpha level at which a term is removed from the model. The default is 0.15 for STEPWISE and

0.10 for BACKWARD. For STEPWISE, K must be greater than or equal to K for AENTER.

ENTER termlist

Specifies the terms that are contained in the starting model for STEPWISE. The ENTER termlist must be a

subset of the TERMS termlist or in the default term list in the design.

FORCE termlist

Specifies the terms to be forced in the model. The FORCE termlist must be a subset of the TERMS termlist

or in the default term list in the design.

NOHIERARCHICAL

Specifies that the model selection procedure does not consider hierarchy.

HIERARCHICAL

Maintains a hierarchical model in stepwise regression. In a hierarchical model, if a higher-order term is

included, all lower-order terms that comprise the higher-order term also appear in the model. For example,

a model that includes the interaction term A*B*C is hierarchical if it includes the following main effects and

lower-order interactions: A, B, C, A*B, A*C, and B*C.

CATONLY

Specifies that only the categorical terms in the model have to be hierarchical.

ALLTERMS

Specifies that both categorical and continuous terms have to be hierarchical.

ATEND

Specifies that the final step of the stepwise procedure adds terms to make the model hierarchical.

ALWAYS

Specifies that the model is hierarchical at every step.

SINGLE

Specifies that only one term can enter the model at each step. So a higher-order term can enter

the model only if the terms that comprise the term are already in the model. For example, the

algorithm does not consider the addition of A*B unless A and B are already in the model.

BACKWARDS does not use SINGLE or MULTIPLE because terms only exit the model.

MULTIPLE

Specifies that multiple terms can enter the model at each step. So a higher order term can enter

the model, and the terms that comprise the term enter the model at the same time. For example,

if A*B is the most statistically significant term, A*B enters the model. At the same time, A and B

enter the model if those terms are not in the model already.

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BACKWARDS does not use SINGLE or MULTIPLE because terms only exit the model.

Validation

TEST C K

TEST K [K]

Specifies to perform validation with a test data set. When you specify a column you select the rows to include in

the test data set. The column has two distinct values. Use the constant to specify which value is for the test data

set.

When you specify a constant first, Minitab Statistical Software randomly selects the data for the test data set. The

constant is the fraction of the data in the test data set. The optional constant is a seed for the random number

generator. If you specify the seed, you can get the same test data set again by specifying the same seed.

CVTEST C

CVTEST K [K]

Specifies to perform k-fold cross validation. When you specify a column, you select the rows for each fold. Rows

with the same value in the column are in the same fold.

When you specify a constant, Minitab Statistical Software randomly selects the data for the folds. The constant

is the number of folds. The optional constant is a seed for the random number generator. If you specify the seed,

you can get the same folds again by specifying the same seed.

Graphs

When you use TEST, the residual plots include separate displays for the training data set and the test data set.

GPARETO

Displays a Pareto chart of the absolute effects. GPARETO draws a vertical reference line on the plot at the margin

of errors. This plot lets you look at both the magnitude and the importance of an effect at the same time. The

alpha level is 1– the confidence level that follows CONFIDENCE, unless you use stepwise selection. For forward

selection, the alpha level is the level that follows AENTER. For backward and stepwise selection, the alpha level is

the level that follows AREMOVE.

RTYPE K

Specifies the type of residual to plot with the graph subcommands.

Type of residualsValue of K

Regular or raw residuals (RESIDUALS)1 (default)

Standardized residuals (SRESIDUALS)2

Deleted Studentized residuals (TRESIDUALS)3

GHISTOGRAM

Displays a histogram or individual value plot of the residuals, depending on the sample size.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Plots the residuals versus the fitted values.

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GORDER

Plots the residuals versus the order of the data. The row number for each data point is shown on the x-axis ( for

example, 1 2 3 4... n).

GFOURPACK

Displays a layout of a histogram of the residuals, a normal probability plot of the residuals, residuals vs fitted

values, and residuals vs order of the data.

GVARIABLE C...C

Displays a separate graph for the residuals versus each specified column.

Results

NODEFAULT

Specifies that no default tables or graphs will be displayed

TBASIC

Displays the tables for the Minitab 16 version of REGRESS.

TMETHOD

Displays the method table.

TMSDETAILS

Displays the type of stepwise procedure and the alpha values to enter and/or remove a predictor from the model.

Use FULL to display the coefficients, p-values, Mallows' Cp, and model summary statistics for each step of the

procedure. Use NOFULL to hide these statistics. If you do not specify FULL or NOFULL, Minitab uses the preferences

set in File > Options > Linear Models > Stepwise.

TEQUATION

Displays the regression equation table. Minitab will display up to 50 equations. If you want to see a single equation,

rather than a separate equation for each factor level combination, use SINGLE. To see the separate equations, use

SEPARATE. If you do not specify SINGLE or SEPARATE, Minitab uses the preferences set in File > Options > Linear

Models > Display of Results.

TCOEFFICIENTS

Displays the table of coefficients. Use FULL to display the full set of coefficients for categorical predictors. Use

NOFULL to show only the linearly independent coefficients. If you do not specify FULL or NOFULL, Minitab uses

the preferences set in File > Options > Linear Models > Display of Results.

TSUMMARY

Displays the summary of model table.

TANOVA

Displays the ANOVA table.

TDIAGNOSTICS

Displays a table of diagnostics. K = 0 displays diagnostics for only unusual observations. K = 1 displays diagnostics

for all observations. If you do not specify K, Minitab uses the preference set in File > Options > Linear Models

> Display of Results.

TDW

Displays Durbin-Watson statistics.

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TSIMPLE

Displays the simple versions of the ANOVA table, table of coefficients, model summary table, and table of unusual

observations.

TEXPAND

Displays the expanded version of the ANOVA table, table of coefficients, model summary table, and table of

unusual observations.

Storage for Box-Cox transformation

BCRESP C

Stores the Box-Cox transformation of the response in C.

BFITS

Stores the fits for the original response.

Storage for fits and residuals

Use to store the fits and residuals in the specified column. When you use TEST , the following statistics are missing

for the test data set: studentized residuals, Cook's distance, and DFITS. The Durbin-Watson statistic is for the training

data set.

FITS C

Stores the fitted values.

RESIDUALS C

Stores the residuals (fitted values – observed values).

SRESIDUALS C

Stores the standardized residuals.

TRESIDUALS C

Stores the deleted Studentized residuals.

HI C

Stores the leverages.

COOK C

Stores Cook's distance.

DFITS C

Stores the DFITS.

SDW C

Stores the Durbin-Watson statistic.

Storage for characteristics of the estimated equation

Use to store characteristics of the estimated equation in the specified column.

COEFFICIENTS C

Stores the estimated coefficients.

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FITS C

Stores the fitted values, often called the Y-hats ( ).

MSE K

Stores the mean square error. When you use TEST, the statistic is for the training data set.

SPVALUE C

Stores the p-values for each predictor.

SVIF C

Stores the VIFs for each predictor.

SS C

Stores the standard deviation of the error term. When you use TEST, the first row is for the training data set and

the second row is for the test data set.

SRSQ C

Stores the R-squared for the model. When you use TEST, the first row is for the training data set and the second

row is for the test data set.

SRSADJ C

Stores the adjusted r-squared for the model. When you use TEST, the statistic is for the training data set.

SPRESS C

Stores the PRESS statistic for the model. When you use TEST, the statistic is for the training data set.

XMATRIX M

Stores the design matrix for regression model.

XPXINV M

Stores a p x p matrix, inverse of X'X. This matrix, when multiplied by MSE is the variance-covariance matrix of the

coefficients. If the WEIGHTS subcommand is used, then XPXINV stores the inverse of the X'WX. When you use

TEST, the matrix is for the training data set.

RMATRIX M

Stores the R matrix of the QR decomposition, also known as the Cholesky decomposition. When you use TEST,

the matrix is for the training data set.

Storage for validation

Use the following commands to store statistics for validation with a test data set or validation with k-fold cross validation.

SSAMPLE C

When CVTEST is used:

•

The storage column, Sample_Id, stores the fold identification in C.

•

C contains values 1, 2, ..., K.

When TEST is used:

•

The storage column, Sample_Id, stores the training and test sample assignments in C.

•

C is a binary text column that contains Training and Test as the two values.

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CVFIT C

Stores the fitted values from each fold's turn as the test data set.

CVRESID C

Stores the residuals from each fold's turn as the test data set.

BREG: Session command for performing best subsets

regression

BREG C C...C

Performs best subsets regression, using the maximum R-squared criterion.

Suppose you specify m predictors. BREG first looks at all one-predictor regression models, selects the model with

the largest R-squared, and displays information on this model and the next best one-predictor model. Then BREG

looks at all two-predictor models, selects the model with the largest R-squared, and displays information on this

and the next best one. The process continues until all m predictors are used.

BREG is an efficient way to select a group of "best subsets" for further analysis by selecting the smallest subset

that fulfills certain statistical criteria. The subset model may actually estimate the regression coefficients and

predict future responses with smaller variance than the full model using all predictors.

INCLUDE C...C

Includes the predictors specified in C...C in all models. Only columns that were specified with the BREG

command can be specified in INCLUDE.

NVARS K [K]

By default, displays the best one-predictor models, the best two-predictor models, on up to the best

m-predictor models. If you specify, for example, NVARS 5 12, then only the best 5-, 6-, ..., 12-predictor

models are displayed.

Note NVARS does not count the predictors given in INCLUDE. Thus, INCLUDE C1–C3 with NVARS 2 6, displays models

with 3 + 2 = 5 to 3 + 6 = 9 predictors.

BEST K

Displays information from the "best" K models of each size, if there are that many. K can be from 1 to 5. The

default is 2.

NOCONSTANT

When you use NOCONSTANT, Minitab omits the b 0 term (the intercept) from the equation. Thus, Minitab

fits the model:

Y = β

+β

+...+ β

+ e

When NOCONSTANT is specified, Minitab does not display R-squared and adjusted R-squared, since their

interpretation is difficult.

You can also use NOCONSTANT as a main command. In that case, it applies to all BREG, REGRESS, and

STEPWISE commands that follow.

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CONSTANT

When you use CONSTANT, Minitab includes the b 0 term (the intercept) in the equation. Thus, Minitab fits

the model:

Y = β

+ β

+β

+...+ β

+ e

You can also use CONSTANT as a main command. In that case, it applies to all BREG, REGRESS, and STEPWISE

commands that follow.

NOWARN

When you specify more than 14 free variables with BREG, Minitab displays a prompt that asks whether you

want to continue with BREG computations even though it will take a long time. Use NOWARN if you want

to suppress this warning prompt.

TEXPAND

Displays the expanded table that includes PRESS, AICc, BIC, and condition number. You can also set this

option in File > Options.

FITLINE: Session command for creating a fitted line

plot

FITLINE C C

Plots the regression line through the actual data or the log 10 of the data. The fitted line plot shows you how

closely the actual data lie to the fitted regression line. You can include only one predictor in the model.

Polynomial regression is one method for modeling curvature in the relationship between a response variable (Y)

and a predictor variable (X). Polynomial modeling techniques try to account for the curvature by extending the

simple linear regression model to include higher powers of X - such as X-squared - as predictors.

Use the POLY subcommand to specify the order of the polynomial you want to fit. If you have not transformed

the X or Y variables, you can fit the following models with FITLINE:

Statistical modelPOLY KOrderModel type

Y = β

+ β

X + e1FirstLinear

Y = β

+ β

X + β

+ e2SecondQuadratic

Y = β

+ β

X + β

+ β

+ e3ThirdCubit

You can generate additional models by using the log

of X and/or Y for linear, quadratic, and cubic models. These

models provide another way of modeling curvature. In addition, taking the log

of Y can reduce right-skewness

and some forms of heteroskedasticity, such as unequal variances.

RTYPE K

Specifies the type of residual to plot with the graph subcommands.

Type of residualsValue of K

Regular or raw residuals (RESIDUALS)1 (default)

Standardized residuals (SRESIDUALS)2

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Type of residualsValue of K

Deleted Studentized residuals (TRESIDUALS)3

GHISTOGRAM

Displays a histogram of the residuals.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Plots the residuals versus the fitted values.

GORDER

Plots the residuals versus the order of the data. The row number for each data point is shown on the x-axis

(for example, 1 2 3 4 ... n).

GFOURPACK

Displays a layout of a histogram of the residuals, a normal probability plot of the residuals, residuals vs fitted

values, and residuals vs order of the data.

GVARIABLE C...C

Displays a separate graph for the residuals versus each specified column.

Options

POLY K

Specifies the order of the polynomial model you want to fit and plot. K is an integer from 1 to 3. The default is 1.

LOGY

Uses log

Y as the response variable.

LOGX

Uses log

X as the predictor variable. If LOGX is used with polynomials of order greater than one, then the

polynomial regression will be based on powers of the log

YSCALE

Plots the transformed response and predictor variables on a log scale. FITLINE ignores YSCALE if you do not use

LOGY.

XSCALE

Plots the transformed response and predictor variables on a log scale. FITLINE ignores XSCALE if you do not use

LOGX.

Displays confidence bands about the fitted regression line.

Displays prediction bands about the fitted regression line.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

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Storage

RESIDUALS C

Stores the residuals in C. If you specified LOGY, Minitab stores the log-transformed residuals. The residual for the

i th log-transformed response is:

= (log

– log

10 i

)

FITS C

Stores the fitted values, often called the Y-hats ( ), in C. If you specified LOGY, Minitab stores the log-transformed

fits (log

COEFFICIENTS C

Stores the estimated coefficients in C.

BRESIDUALS C

Stores in C the residuals of the transformed response in the original scale of the data. This subcommand is only

available if you used LOGY.

BFITS C

Stores in C the fitted values of the transformed response in the original scale of the data. This subcommand is

only available if you used LOGY.

GSAVE "file_name"

GSAVE K

Saves the graph in a file.

The default file name is Minitab.PNG. You can specify a custom file name in double quotation marks ("file_name"),

or as a stored text constant (K). You can also use any of the following subcommands to save the graph in a different

graphics format.

Some graph commands—for example, HISTOGRAM C1 C2 C3—generate more than one graph. If you include

the GSAVE subcommand with such a command, Minitab saves multiple files. Minitab gives each file a different

file name. Minitab uses the first five characters of the name you specify, then appends a number (001, 002, and

so on), for up to 300 files.

JPEG

JPEG color

PNGB

PNG grayscale

PNGC

PNG color

TIFB

TIF grayscale

TIF

TIF color

BMPB

BMP grayscale

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BMPC

BMP color

GIF

EMF

RESOLUTION K

Saves the graph at a resolution of K dots per inch.

TITLE "title"

Specifies a title for the graph. If you do not specify a title, Minitab uses a default title.

WTITLE "title"

You can use WTITLE as a subcommand with LAYOUT and all graphs. The title that you specify becomes the

command title of the resulting graph.

SSWORKSHEET: Session command for creating a

stability study worksheet

Important SSWORKSHEET writes data to the active worksheet. To prevent possible confusion and lost data, it is best to use NEW immediately

prior to SSWORKSHEET. (Do not use NEW however, if you use the TIME subcommand.) NEW creates a new, blank worksheet and makes the

new worksheet the active worksheet.

SSWORKSHEET K K K

Creates a stability study worksheet with the following parameters (in the following order):

•

The number of times as K

•

The number of batches as K

•

The number of replicates per batch at each time as K

TIME C

TIME K...K

Optional subcommand TIME specifies the test times as values from column C or as constants K...K.

SSWORKSHEET K K

Creates a stability study worksheet with the following parameters (in the following order):

•

The number of batches as K

•

The number of replicates per batch at each time as K

TIME C...C (required)

Required subcommand TIME specifies a column of test times for each batch

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Options

BNAME C

BNAME K...K

Specifies a name for each batch. The default batch names are the text values "1", "2", "3", ... .

TSUMMARY

Displays a summary of the design that includes the number of sampling times, the number of batches, the number

of samples per batch at each time, and the total number of runs.

SORDER C

Stores the standard order of the runs in column C.

RORDER C

Stores the run order in column C.

XMATRIX C C

Stores the sampling times for each run in the first column C and the batch names in the second column C.

Randomization

Use the following commands to control how the runs are randomized in the worksheet. If you do not use any of these

subcommands, then the runs are not randomized, so the run order is the same as the standard order.

To generate the same random order, you can optionally set the base as K, where K is an integer that is greater than

RANDOMIZE [K] (default)

RANDOMIZE specifies to randomize the order of the batches at each test time, and the repeats at each test time.

BRANDOMIZE [K]

BRANDOMIZE specifies to randomize the order of the batches at each test time, but to keep all repeats from one

batch together before switching to the next batch.

NRANDOMIZE

NRANDOMIZE specifies to sample each batch in sequential order and to take all repeats from one batch before

switching to the next batch.

SHELFLIFE: Session command for performing a

stability study

SHELFLIFE

Analyzes the stability of a product over time to determine the product's shelf life. Minitab fits a linear model to

represent the relationship between the response variable, the time variable, and an optional batch factor. The

batch factor can be fixed or random. Based on the model, Minitab calculates the shelf life, which is the length of

time that the response is expected to remain within the specifications.

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Options

RESPONSE C

Specifies the column that contains the response variable. The column must be numeric or date/time.

TIME C (required)

Specifies the time variable. The column must be numeric or date/time and must match the length of the response

column.

BATCH C

Specifies the categorical batch factor, if you have one. The column can be numeric, text, or date/time and must

match the length of the response column.

RANDOM

Specifies that BATCH is a random factor. If BATCH is not issued, RANDOM is ignored. For random batches, Minitab

uses an iterative algorithm to estimate the variance components and to select the model. There can be cases

where the variance components cannot be estimated and no further analysis is possible.

MAXITER K

Specifies the maximum number of iterations. K must be a nonnegative integer.

STARTING K K K

Specifies the starting estimates for the variance components. If you do not use STARTING, Minitab uses the

minimum norm quadratic unbiased estimation (MINQUE) estimates. The MINQUE estimates are typically

good estimates.

CTOLERANCE K

Specifies the convergence tolerance value for object function. K must be greater than 0. If this subsubcommand

is not issued, Minitab uses the value 1E-7.

ETOLERANCE K

Specifies the convergence tolerance value for estimates. K must be greater than 0. If this subsubcommand

is not issued, Minitab uses the value 1E-7.

LSPEC K

Specifies the lower specification limit. You must use LSPEC, USPEC, or both. If both LSPEC and USPEC are issued,

USPEC must be greater than LSPEC. K must be numeric.

USPEC K

Specifies the upper specification limit. You must use LSPEC, USPEC, or both. If both LSPEC and USPEC are issued,

USPEC must be greater than LSPEC. K must be numeric.

BPERCENT K

Specifies using K percent of the response above the lower spec or below the upper spec to estimate shelflife. K

must be between 0 and 100. Minitab uses 50 percent if BPERCENT is not issued.

PVALUE K

Specifies the significance level that is used for model selection. K must be between 0 and 1. Minitab uses 0.25 if

PVALUE is not issued.

ONESPEC

Specifies to use a one-sided significance level when you specify both specification limits. Use this subcommand

when the response variable has two specification limits, but only one limit is relevant for the shelf life. For example,

if the confidence level is 95% and you select this option, then the confidence level for the single bound that is

relevant to the shelf life is also 95%. If you do not select this option, then the confidence level for both bounds

is 97.5%. The single 95% bound gives a longer shelf life.

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For example, a medication has an upper specification limit of 12.5 micrograms and a lower specification limit of

12 micrograms. In storage, the medication degrades, but never increases in strength. Only the lower specification

limit is relevant to the shelf life.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

Box-Cox

BOXCOX [K]

Performs a Box-Cox transformation with a selected lambda. K is the value of lambda and must be between −5

and +5. If K is not given and you have a fixed batch, Minitab will find the optimal lambda. By default, Minitab

rounds the optimal value.

Minitab cannot calculate the optimal lambda when batch is a random factor. Consequently, you must specify a

lambda value for BOXCOX if you use RANDOM.

Graphs

GSHELFLIFE

Displays the shelf life plot. Use the subsubcommands to control how the batches are displayed.

INDIVIDUAL

Displays a graph for each individual batch.

ALL [K]

Displays all batches on one graph. If you specify K, each graph displays up to K batches.

RTYPE K

Specifies the type of residual to plot with the graph subcommands in K. If MARGINAL is issued, K must be 1 or 2.

Type of residualsValue of K

Regular or raw residuals (RESIDUALS)1 (default)

Standardized residuals (SRESIDUALS)2

Deleted Studentized residuals (TRESIDUALS)3

MARGINAL

Displays marginal residuals on the residual plots. If MARGINAL is not issued, the residual plots display

conditional residuals for random batches.

GHISTOGRAM

Displays a histogram or individual value plot of the residuals, depending on the sample size.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Plots the residuals versus the fitted values.

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GORDER

Plots the residuals versus the order of the data. The row number for each data point is shown on the x-axis (for

example, 1 2 3 4... n).

GVARIABLE C...C

Displays a separate graph for the residuals versus each specified column.

GFOURPACK

Displays a layout that includes a histogram of the residuals, a normal probability plot of the residuals, residuals

vs fitted values, and residuals vs the order of the data.

Results

Displays a variety of tables in the output. The availability of some tables depends on whether BATCH is a fixed factor

or a random factor.

NODEFAULT

Specifies that no default tables and graphs will be displayed.

TMETHOD

Displays the method table.

TFACTOR

Displays the name, number of levels, and the values for the batch factor in your model.

TITERATION

Displays the estimation iteration history table when batch is a random factor.

TVARIANCE

Displays the variance component table when batch is a random factor.

TCOVARIANCE

Displays the asymptotic variance covariance matrix of variance estimates when batch is a random factor.

TMSELECTION

Displays the model selection results, including the terms that are removed from the final model.

TANOVA

Displays the ANOVA table for a fixed batch.

TSUMMARY

Displays the summary of model table.

TCOEFFICIENTS

Displays the table of coefficients. Use FULL to display the full set of coefficients for the batch factor. Use NOFULL

to show only the linearly independent coefficients. If you do not specify FULL or NOFULL, Minitab uses the

preferences set in File > Options > Linear Models > Display of Results.

FULL

Specifies to display the full set of coefficients for the batch factor

NOFULL

Specifies to not display the full set of coefficients for the batch factor

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TRANDOM

Displays the random effect prediction table when batch is a random factor.

TEQUATION

Displays the regression equation table, or the marginal fitted equation table if batch is a random factor.

TDIAGNOSTICS [K]

Displays a table of fits and diagnostics. K = 0 displays diagnostics for only unusual observations. K = 1 displays

diagnostics for all observations. If you do not specify K, Minitab uses the preference set in File > Options > Linear

Models > Display of Results.

TCONDITIONAL [K]

Displays the conditional fits and residuals when batch is a random factor. K = 0 displays diagnostics for only

unusual observations. K = 1 displays diagnostics for all observations. If you do not specify K, Minitab uses K = 0.

TSHELFLIFE

Displays the shelf life table.

TSIMPLE

Displays the simple versions of the ANOVA table, table of coefficients, model summary table, and table of unusual

observations.

TEXPAND

Displays the expanded version of the ANOVA table, table of coefficients, model summary table, and table of

unusual observations.

Storage

Use to store analysis results in the specified column or matrix. The availability and function of the storage commands

depends on whether BATCH is a fixed factor or a random factor.

RESIDUALS C

Stores the residuals (fitted values – observed values) if batch is a fixed factor. Marginal residuals (marginal fitted

values – observed values) if batch is a random factor.

SRESIDUALS C

Stores the standardized residuals if batch is a fixed factor. Standardized marginal residuals if batch is a random

factor.

TRESIDUALS C

Stores the deleted Studentized residuals of the fit if batch is a fixed factor. If batch is a random factor, stores the

studentized marginal residuals.

CRESIDUALS C

Stores the conditional residuals when batch is a random factor.

CSRESIDUALS C

Stores the conditional standardized residuals when batch is a random factor.

FITS C

Stores the fitted values if batch is a fixed factor. Marginal fitted values if batch is a random factor.

CFITS C

Stores the conditional fitted values when batch is a random factor.

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HI C

Stores the leverages.

COOK C

Stores Cook's distance.

DFITS C

Stores the DFITS.

COEFFICIENTS C

Stores the estimated coefficients.

BLUP C

Stores the best linear unbiased predictor when batch is a random factor.

XMATRIX M

Stores the design matrix for fixed effects terms.

ZMATRIX M

Stores the design matrix for random effects terms.

COVARIANCE M

Stores the variance-covariance matrix of the variance component estimates if batch is a random factor.

FCOVARIANCE M

Stores the covariance matrix of fixed effect estimates.

NLINEAR: Session command for performing

nonlinear regression

NLINEAR

Fits regression models in which the expected value of the response is a nonlinear function.

Use nonlinear regression to mathematically describe the nonlinear relationship between a response variable and

one or more predictor variables. Specifically, use nonlinear regression instead of ordinary least squares regression

when you cannot adequately model the relationship with linear parameters. Parameters are linear when each

term in the model is additive and contains only one parameter that multiplies the term. Use this procedure for

fitting models that are nonlinear in the parameters, storing regression statistics, examining residual diagnostics,

generating point estimates, and generating prediction and confidence intervals.

Required subcommands

RESPONSE C

Specifies the response column. The column cannot be a text column.

CONTINUOUS C...C

Specifies one or more predictor columns. The columns cannot be text columns, and they must have the same

number of rows as the response column.

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PARAMETER "name" K...K

Use PARAMETER for each parameter in the model. The parameter name can match the name of a variable, but it

cannot match the name of the response column or any predictor columns.

The parameter name must be followed by one or more starting values for the parameter. Minitab does not allow

missing values.

If you give a parameter more than one starting value, Minitab evaluates the residual sum of squares (SSE) at all

combinations, and uses the combination with the smallest value of the SSE.

EXPECTATION expression

Specifies the expectation function. The expression must use all of the parameters and predictors. All columns you

use in the expression must be predictor columns.

If a parameter name looks like a variable name, Minitab interprets it as a variable name. To have Minitab interpret

it as a name, you must put quotes around it. For example, a parameter you named k1 must be referred to as 'k1'.

Options

LOCK parameters

Locks one or more parameters at their starting values. If you give a locked parameter more than one starting

value, the first value is used. You must leave at least one parameter unlocked. For example, LOCK "b1", locks the

parameter b1.

WEIGHT C

Specifies weights for weighted regression in C. Weights must be nonnegative. Rows with zero or missing weight

are omitted. Note that there is no provision for these weights to change from one iteration to the next.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

ITYPE K

Specifies the type of confidence interval.

Type of confidence intervalValue of K

Lower bound–1

Two-sided0 (default)

Upper bound1

CONSTRAINT constraint

Specifies a constraint on a parameter. Repeat CONSTRAINT to specify multiple constraints. Each parameter can

appear one time on one CONSTRAINT subcommand. For example, CONSTRAINT 1200 < 'b1' < 1400

constrains the fitted value of parameter b1 to between 1200 and 1400.

MARQUARDT

Specifies the Levenberg-Marquardt algorithm rather than the default Gauss-Newton algorithm. Use this

subcommand if the Gauss-Newton algorithm fails to converge.

TOLERANCE K [K]

K is the tolerance level for convergence, and it must be a positive number. The default value is 10

−5

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The convergence criterion is the relative offset orthogonality convergence criterion described in Bates and Watts

(1981).

ITERATIONS K

Specifies the maximum number of iterations used in the optimization algorithm. K must be a positive integer. If

you do not specify ITERATIONS, the default value is 200.

Predict

PCONTINUOUS E..E

PCONTINUOUS also computes the standard errors of the fitted values, 95% confidence intervals, and 95% prediction

intervals. The number of arguments must equal the number of predictors in the model. If you include columns,

they must have the same number of rows. Minitab assigns predictors in the same order as they appear on the

CONTINUOUS subcommand.

If you omit PCONTINUOUS and include any of PFITS, PSEFITS, CLIMITS, or PLIMITS, then those subcommands are

ignored.

The prediction interval computed by PREDICT assumes a weight of 1. If you used the WEIGHT subcommand with

values other than 1, you should adjust the prediction interval values manually.

PFITS C

Stores the predicted values in C.

PSEFITS C

Stores the standard errors of the predicted values in C.

CLIMITS C C

Stores the upper and lower limits of the confidence intervals in C C. If you specify -1 or 1 with ITYPE then only

one argument is allowed.

PLIMITS C C

Stores the upper and lower limits of the prediction intervals in C C. If you specify −1 or 1 with ITYPE then only

one argument is allowed.

Tables

TMETHOD

Displays a table of information about the method and options. Includes the type of algorithm used, the maximum

number of iterations, and the tolerance level for convergence.

TSTARTING

Displays the parameters' starting values.

TCONSTRAINTS

Displays a table of constraints for the parameters.

TITERATIONS

Displays the parameter estimates, and the residual sum of squares (SSE) at each iteration.

TEQUATION

Displays the regression equation.

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TPARAMETERS

Displays the parameter estimates and their approximate standard errors.

Adds a confidence interval for each parameter in the parameter estimates table.

TCORRELATION

Displays the correlation matrix for the parameter estimates.

TSUMMARY

Displays a test for lack of fit, and a summary of the fits that includes the number of iterations, SSE, the degrees

of freedom for error, MSE, and S.

TPREDICTIONS

Displays a table of fitted values, confidence intervals, and prediction intervals for the new observations specified

by PCONTINUOUS.

NODEFAULT

Suppresses the display of tables and graphs other than those specifically requested.

Graphs

GHISTOGRAM

Displays a histogram of the residuals.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Plots the residuals versus the fitted values.

GFOURPACK

Displays a layout of a histogram of the residuals, a normal probability plot of the residuals, residuals vs fitted

values, and residuals vs order of the data.

GORDER

Plots the residuals versus the order of the data. The row number for each data point is shown on the x-axis ( for

example, 1 2 3 4... n).

GVARIABLE C...C

Displays a separate graph for the residuals versus each specified column.

GFCURVE

Displays a fitted curve plot. Minitab ignores the subcommand if there is more than one predictor.

GFCI

Overlays the confidence interval bands.

GFPI

Overlays the prediction interval bands.

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Storage

RESIDUALS C

Stores the residuals (fitted values – observed values).

FITS C

Stores the fitted values, often called the Y-hats ( ).

SPARAMETERS C

Stores the estimated parameters.

SSEPARAMETERS C

Stores the standard error of each parameter.

SGRID C...C

Stores each starting value combination and the corresponding initial sum of the squared residuals (SSE). Minitab

performs the nonlinear regression analysis using the starting value combination that produces the smallest initial

SSE.

OREG: Session command for performing orthogonal

regression

OREG

Performs orthogonal regression in which both the response and predictor contain measurement error.

OREG uses orthogonal regression to fit a model to one predictor. Optionally, you can do the following:

•

Store the residuals, fitted values, and variance estimates

•

Generate predicted values for new observations

•

Control display of results

•

Generate several plots for residual analysis. For more information, go to Residual analysis and regression

diagnostics on page 1178.

VARRATIO C

Specifies the error variance ratio (Y/X). K must be greater than 0.

Graphs

RTYPE K

Specifies the type of residual to plot with the graph subcommands in K.

Type of residualsValue of K

Regular or raw residuals (RESIDUALS)1 (default)

Standardized residuals (SRESIDUALS)2

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GHISTOGRAM

Displays a histogram of the residuals.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Plots the residuals versus the fitted values.

GFOURPACK

Displays a layout of a histogram of the residuals, a normal probability plot of the residuals, residuals vs fitted

values, and residuals vs order of the data.

GORDER

Plots the residuals versus the order of the data. The row number for each data point is shown on the x-axis ( for

example, 1 2 3 4... n).

GVARIABLE C...C

Displays a separate graph for the residuals versus each specified column.

GFLINE

Displays a fitted line plot of the original data.

LSFIT

Displays the least squares fitted line on the fitted line plot. When you use LSFIT, both the orthogonal and the least

squares regression equations are displayed on the graph.

Options

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

PREDICT K

PREDICT C

Specifies the value of the predictor X. You can enter a constant or a column of values.

SPREDICT C

Stores the predicted values in the specified column.

SSTDEV C

Stores the standard deviations of the predicted values in the specified column.

Results

REVARRATIO

Displays the error variance ratio.

REQUATION

Displays the orthogonal regression equation.

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RCOEFFICIENT

Displays the coefficient table.

RVARCOMP

Displays the error variance estimates.

RFITRESID

Displays the fitted values for the true predictor and true response as well as the residuals.

Storage

SRESIDUALS C

Stores the residuals in the specified column.

SSRESIDUALS C

Stores the standardized residuals in the specified column.

SCOEFFICIENT C

Stores the intercept and slope in the specified column.

SVARIANCE C

Stores the error variances for the predictor and response in the specified column.

SXFIT C

Stores the fitted values of the predictor.

SYFIT C

Stores the fitted values of the response.

PLS: Session command for performing partial least

squares regression

PLS C...C = termlist

Use partial least squares regression (PLS) to relate a set of predictors to one or more response variables. Use PLS

with ill-conditioned data, when predictors are highly collinear or predictors outnumber observations. Predictors

can be either continuous or categorical. Use NCOMPONENTS to specify the number of components to use in the

model. Use XVALIDATION to select the number of components that maximize the model's predictive ability. Use

PCONTINUOUS and PCATEGORICAL to calculate fitted values for new observations using the PLS model.

CONTINUOUS C...C

Specifies which columns of variables are continuous. Columns must contain numerical or date/time data.

CATEGORICAL C...C

Specifies which, if any, columns of variables are categorical. Columns may be numerical, text, or date/time.

NCOMP K

Sets the maximum number of components to extract or cross-validate, which cannot exceed the number of

predictors in your model or the number of observations minus one. Minitab extracts 10 or cross-validates

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10 components by default, unless the number of predictors or the number of observations minus 1 is less

than 10.

Options

XVALIDATION K

XVALIDATION C

XVALIDATION estimates your model's predictive ability by recalculating your model, leaving 1 or more observations

out each time.

Use K to specify the number of observations to leave out each time the model is recalculated. Specifying 1 means

that the model is recalculated as many times as there are observations.

Use C as a group identifier to indicate which observations are deleted together each time the model is recalculated.

Observations with identical numbers in C are removed at the same time. The column must contain positive integers

and equal the length of the predictor and response columns.

CODING K

CODING specifies the type of coding to use for categorical predictors.

•

K = −1 for (−1, 0, +1) coding

•

K = 1 for (1, 0) coding

REFERENCE C K...C K

REFERENCE changes the default coding for the categorical predictor columns. To change the default reference

level, specify the categorical predictor column followed by the reference level. (You must enclose text and

date/time levels in double quotes.) You can assign a reference level only when you use 1, 0 coding.

Prediction

Computes the fitted Ys, or for new observations using your PLS model.

PCONTINUOUS E...E

Use PCONTINUOUS for new continuous predictors.

PCONTINUOUS displays a table that contains the fitted Y's, standard errors of the fitted Y's, a 95% confidence

interval, and a 95% prediction interval. E...E may be a list of constants or a list of columns, or a mixture of constants

and columns. The number of predictors must equal the number of predictors in your PLS model.

PCATEGORICAL E...E

Use PCATEGORICAL for new categorical predictors.

PCATEGORICAL displays a table that contains the fitted Y's, standard errors of the fitted Y's, a 95% confidence

interval, and a 95% prediction interval. E...E may be a list of constants or a list of columns, or a mixture of constants

and columns. The number of predictors must equal the number of predictors in your PLS model.

RESPONSE C...C

Specifies response values for your new observations. Minitab calculates a test R

that indicates how well the model

fits new observations.

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CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

PFITS C...C

Stores the fits from PCONTINUOUS and PCATEGORICAL.

PSEFITS C...C

Stores the estimated standard errors of the fits from PCONTINUOUS and PCATEGORICAL.

CLIMITS C, C...C, C

Stores the lower and upper confidence limits from PCONTINUOUS and PCATEGORICAL.

PLIMITS C, C...C, C

Stores the lower and upper prediction limits from PCONTINUOUS and PCATEGORICAL.

Storage of fits and residuals

FITS C...C

Stores the fitted values, often called the Y-hats ( ).

CVFITS C...C

Stores the cross-validated fitted values.

RESIDUALS C...C

Stores the residuals (fitted values – observed values).

PRESIDUALS C...C

Stores the cross-validated residuals.

SRESIDUALS C...C

Stores the standardized residuals.

Storage of model information

COEFFICIENTS C...C

Stores the estimated coefficients, one stored column for each response.

SCOEFFICIENTS C...C

Stores the standardized coefficients, one stored column for each response.

HI C

Stores the severage values.

XDISTANCE C

Stores the distances from the x-model.

YDISTANCE C

Stores the distances from the y-model.

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Storage of component information

XSCORES M

Stores the X-scores.

YSCORES M

Stores the Y-scores.

XLOADINGS M

Stores the X-loadings.

YLOADINGS M

Stores the Y-loadings.

XWEIGHTS M

Stores the X-weights.

XRESIDUALS M

Stores the X-residuals.

YRESIDUALS C...C

Stores the Y-residuals, one stored column for each response.

XCALCULATED M

Stores the X-calculated values.

YCALCULATED C...C

Stores the Y-calculated values, one stored column for each response.

Graphs for model evaluation

GSELECTIONPLOT

Displays a scatterplot of R

and predicted R

values vs the number of components

GFIT

Displays a scatterplot of fitted and cross-validated responses vs observed responses

GFCOEFFICIENT

Displays a projected scatterplot of the coefficients

GCCOEFFICIENT

Displays a projected scatterplot of the standardized coefficients

GDISTANCE

Displays a scatterplot of each observation's distance from the x-model and the y-model

Graphs for residual analysis

GHISTOGRAM

Displays a histogram of the standardized residuals

GNORMAL

Displays a normal probability plot of the standardized residuals

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GRESIDUALS

Displays a scatterplot of residuals vs fitted values

GLEVERAGE

Displays a scatterplot of standardized residuals vs leverages

GFOURPACK

Displays a layout of a histogram of the standardized residuals, a normal probability plot of the standardized

residuals, standardized residuals vs fitted values, and standardized residuals vs order of the data.

Graphs for model evaluation

GSCORE

Displays a plot of the x-scores of the first component versus the x-scores of the second component

GTHREEDPLOT

Displays a three-dimensional plot of the x-scores of the first component, second and third components

GLOADING

Displays a plot of the x-loadings of the first component vs the x-loadings of the second component

GXRESIDUALS

Displays a line plot of the x-residual matrix

GXCALCULATED

Displays a line plot of the x-calculated matrix.

Results

TMETHOD

Displays the number of components and analysis of variance table.

TANOVA

Displays the analysis of variance table.

TSELECTION

Displays the model selection and validation table.

TCOEFFICIENTS

Displays the unstandardized and standardized coefficients.

TFITS

Displays the table of fitted values and residuals.

TLEVERAGES

Displays the leverages and distances from the x- and y-model.

TPREDICTION

Displays the prediction table.

TXSCORES

Displays the x-score matrix.

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TYSCORES

Displays the y-score matrix.

TXLOADINGS

Displays the x-loading matrix.

TYLOADINGS

Displays the y-loading matrix.

TXRESIDUALS

Displays the x-residual matrix.

TYRESIDUALS

Displays the y-residual matrix.

TXCALCULATED

Displays the x-calculated matrix.

TYCALCULATED

Displays the y-calculated matrix.

GZLM: Session command for fitting a binary logistic

model or a Poisson model

GZLM

Creates a generalized linear model with the distribution model that you specify: logistic or Poisson. GZLM fits a

model with one or more predictors using an iterative-reweighted least squares algorithm to obtain maximum

likelihood estimates of the parameters. Use the logistic model for a binary response variable. Use the Poisson

model for a Poisson response variable.

The predictors can be either factors (categorical predictors) or covariates (continuous predictors). Factors can be

crossed. Covariates can be crossed with each other or with factors. For more information, go to How to specify

the model for ATCLASS, GZLM, OLOGISTIC and NLOGISTIC on page 1165. You can use stepwise methods to explore

different terms in the model. You can use a test data set or k-fold cross-validation to validate the results.

Factor columns can be numeric or text. For a numeric factor, Minitab designates the lowest numeric value as the

reference level. For a text factor, Minitab determines the reference level alphabetically. For an example, go to

Entering data for factor variables on page 1142.

When the response variable column is binary, the highest value is the reference event. When the response variable

column is text, reverse alphabetical order determines the event. For example, if you enter the response variable

as success and failure, success is the event. For examples and information on entering the response data, go to

Entering data for response variables on page 1142.

This analysis provides diagnostic plots, goodness-of-fit tests, and other diagnostic measures so you can assess

the validity of your model. For more information, go to Generalized linear model diagnostics and residual analysis

on page 1177.

Minitab excludes all observations with missing values on either the response variable or any of the predictors

from all calculations.

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BINOMIAL

Analyzes the relationship between a binary response variable that you specify in RESPONSE and the predictors

that you with CONTINUOUS and CATEGORICAL. You specify the model with TERMLIST.

LOGIT

Use the logit link function.

NORMIT

Use the normit link function.

GOMPIT

Use the gompit link function. The gompit link function is also called the complementary log-log link

function.

POISSON

Analyzes the relationship between a Poisson response variable that you specify in RESPONSE and the predictors

that you with CONTINUOUS and CATEGORICAL. You specify the model with TERMLIST.

LOG

Use the natural log link function.

SQRT

Use the square root link function.

IDENTITY

Use the identity link function. The identity link function does not transform the response.

RESPONSE C [C]

Enters the response data as counts. With BINOMIAL, you can enter a column that contains the number of

events first and then a column that contains the number of trials. For examples, go to Entering data for

response variables on page 1142.

CONTINUOUS C...C

Specifies the continuous predictors are in columns C...C. The columns must be numeric or date/time and

must match the length of the response column.

CATEGORICAL C...C

Specifies the categorical predictors are in columns C...C. The columns can be numeric, text, or date/time and

must match the length of the response column.

TERMS termlist

Specifies the model terms. Terms must be legal cross-terms. Only continuous predictors may be repeated.

Nested terms are not allowed. The model can be nonhierarchical. For examples, go to How to specify the

model for ATCLASS, GZLM, OLOGISTIC and NLOGISTIC on page 1165.

Options

FREQUENCY C

Indicates how often the data in a row occur. For examples, go to Entering data for response variables on page

1142.

CONSTANT

Fits the model with the intercept.

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When you use CONSTANT, Minitab includes the intercept term in the model. For example, for a binomial logistic

regression, Minitab fits the model

g(p) = β

+ x'β

If you do not specify CONSTANT or NOCONSTANT, then Minitab includes the intercept.

NOCONSTANT

Fits the model without the intercept.

When you use NOCONSTANT, Minitab omits the intercept term.

g(p) = x'β

If you do not specify CONSTANT or NOCONSTANT, then Minitab includes the intercept.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

ITYPE K

Specifies the type of confidence interval.

Type of confidence intervalValue of K

Lower bound–1

Two-sided0 (default)

Upper bound1

WALD

Use the Wald test for the ANOVA table.

LRT

Use the Likelihood ratio test for the ANOVA table.

ADJDEVIANCE

Use adjusted deviances for the tests of significance in the ANOVA table when the Likelihood ratio test is used.

SEQDEVIANCE

Use sequential deviances for the tests of significance in the ANOVA table when the Likelihood ratio test is used.

The order of the terms in TERMLIST affects the statistical significance. The default is to use adjusted deviances.

PEARSON

Specifies the use of Pearson residuals. The default is to use deviance residuals.

WEIGHT C

Performs a weighted regression. An n x n matrix W is formed with the column of weights as its diagonal and zeros

elsewhere. The regression coefficients are estimated by:

(X' W X)

−1

(X' W Y)

EFFECT

Specifies an effect coding scheme for categorical predictors (+1, 0, −1). With effect coding, the coefficients compare

the levels of the categorical variable to the mean. The default is binary coding (1, 0). With binary coding, the

coefficients compare the levels of the categorical variable to a reference level.

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HGROUP K

Specifies the number of groups for the Hosmer-Lemeshow goodness-of-fit test with K. The default is 10.

REFERENCE C K...C K

Specifies the reference level for categorical predictor C with K. The coefficients compare the other levels of

categorical variables to the reference level when you use binary coding.

LOCATION [K...K]

Standardize continuous predictors by subtracting the means. Optionally, you can subtract different constants that

you specify with K...K. The order of the constants matches the order of the columns after CONTINUOUS.

SCALE [K...K]

Standardize continuous predictors by dividing by the standard deviations. Optionally, you can divide by different

constants that you specify with K...K. The order of the constants matches the order of the columns after

CONTINUOUS.

LEVELS K K...K K

Standardize continuous predictors by coding the low and high levels K K of each predictor to −1 1. The order of

the constants matches the order of the columns after CONTINUOUS.

UNSTANDARDIZED

Use the original units for the continuous predictors. If you do not specify LOCATION, SCALE, LEVELS, or

UNSTANDARDIZED, then Minitab uses the preferences that are set in File > Options > Linear Models > Coding

of Predictors.

REVENT K

Specifies which is the response event with K. The value of K matches one of the values in the response column.

Enclose text values in quotation marks. For example, to model the probability of a "Fail" in the response column,

enter REVENT "Fail".

EVNAME K

When the RESPONSE command has two columns, you can specify the name of the event with K. Enclose text

values in quotation marks.

ITERATION K

Specifies the maximum number of iterations for the optimization algorithm that estimates the coefficients. The

default is 50.

TOLERANCE K

Specifies the tolerance level to keep a predictor in the model that is either highly correlated with another predictor

or nearly constant. By default, K = 4 * 2.22E–012. K must be a positive number. Lowering tolerance by giving a

very small argument can prevent Minitab from eliminating problematic predictor columns from the model.

Correlated predictors

1 – R-squared, where R-squared is the value resulting from regressing one predictor on the remaining

predictors. If (1 – R-squared) is < K, Minitab removes the predictor from the equation.

Constant predictors

The parameter for forcing in a variable that is nearly constant. If the coefficient of variation of a predictor

(the standard deviation / the mean) is < square root K, Minitab considers the predictor to be essentially

constant and removes it from the equation.

CTOLERANCE K

Specifies the convergence criterion for the algorithm that estimates the coefficients. By default, K = 1E-8.

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START C

Specifies a column that contains starting values for the coefficients in the optimization algorithm. The column

begins with the estimate of the constant, then follows the order of the model after TERMS.

Stepwise selection

STEPWISE, FORWARD, BACKWARD, FINFORMATION and FVALIDATION perform a stepwise regression procedure to

fit the model. No arguments are needed for these subcommands.

STEPWISE

Specifies a stepwise model selection procedure that uses both forward selection and backward elimination. If you

do not include subcommands about hierarchy, STEPWISE and FORWARD add 1 term at a step and maintain model

hierarchy, the equivalent of the following:

HIERARCHICAL; ALLTERMS; ALWAYS; SINGLE.

FORWARD

Specifies a stepwise model selection procedure that uses forward selection. If you do not include subcommands

about hierarchy, STEPWISE and FORWARD add 1 term at a step and maintain model hierarchy, the equivalent of

the following:

HIERARCHICAL; ALLTERMS; ALWAYS; SINGLE.

BACKWARD

Specifies a stepwise model selection procedure that uses backward elimination. Removes a single term at each

step and maintains a hierarchical model, the equivalent of the following:

HIERARCHICAL; ALLTERMS; ALWAYS.

FINFORMATION

Specifies a stepwise model selection procedure that uses forward information criteria selection. Use AICCORRECTED

or BICRITERION to specify which information criterion to use to select the final model. If you do not specify a

criterion, Minitab uses AICCORRECTED.

The forward information criteria procedure adds the term with the lowest p-value to the model at each step. If

you do not include subcommands about hierarchy, FINFORMATION adds 1 term at a step and maintains model

hierarchy, the equivalent of the following:

HIERARCHICAL; ALLTERMS; ALWAYS; SINGLE.

Minitab calculates the information criteria for each step.

In most cases, the procedure continues until one of the following conditions occurs:

•

The procedure does not find a new minimum of the criterion for 8 consecutive steps.

•

The procedure fits the full model.

•

The procedure fits a model that leaves 1 degree of freedom for error.

If you specify settings for the procedure that require a hierarchical model at each step and allow only one term

to enter at a time, then the procedure continues until it either fits the full model or fits a model that leaves 1

degree of freedom for error. Minitab displays the results of the analysis for the model with the minimum value

of the selected information criterion, either the corrected Akaike's Information Criterion (AICc) or the Bayesian

Information Criterion (BIC).

AICCORRECTED

Specifies the use of the corrected Akaike's Information Criterion (AICc) to select the final model.

BICRITERION

Specifies the use of the Bayesian Information Criterion (BIC) to select the final model.

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FVALIDATION

Specifies the use of forward selection with the test deviance R

as the criterion for the selection of the final model.

When you use FVALIDATION, you must also use TEST. FVALIDATION is not compatible with FREQUENCY or

ATEND.

When you use TEST, the procedure is similar to forward selection for other methods. At the end of each step,

Minitab Statistical Software calculates the test R

statistic. At the end of the forward selection procedure, the

model with the greatest test R

value is the final model. FVALIDATION stops under the same conditions as

FINFORMATION.

AENTER K

Specifies the alpha level at which a term is entered into the model. The default is 0.15 for STEPWISE and 0.25

for FORWARD.

AREMOVE K

Specifies the alpha level at which a term is removed from the model. The default is 0.15 for STEPWISE and

0.10 for BACKWARD. For STEPWISE, K must be greater than or equal to K for AENTER.

ENTER termlist

Specifies the terms that are contained in the starting model for STEPWISE. The ENTER termlist must be a

subset of the TERMS termlist or in the default term list in the design.

FORCE termlist

Specifies the terms to be forced in the model. The FORCE termlist must be a subset of the TERMS termlist

or in the default term list in the design.

NOHIERARCHICAL

Specifies that the model selection procedure does not consider hierarchy.

HIERARCHICAL

Maintains a hierarchical model in stepwise regression. In a hierarchical model, if a higher-order term is

included, all lower-order terms that comprise the higher-order term also appear in the model. For example,

a model that includes the interaction term A*B*C is hierarchical if it includes the following main effects and

lower-order interactions: A, B, C, A*B, A*C, and B*C.

CATONLY

Specifies that only the categorical terms in the model have to be hierarchical.

ALLTERMS

Specifies that both categorical and continuous terms have to be hierarchical.

ATEND

Specifies that the final step of the stepwise procedure adds terms to make the model hierarchical.

ALWAYS

Specifies that the model is hierarchical at every step.

SINGLE

Specifies that only one term can enter the model at each step. So a higher-order term can enter

the model only if the terms that comprise the term are already in the model. For example, the

algorithm does not consider the addition of A*B unless A and B are already in the model.

BACKWARDS does not use SINGLE or MULTIPLE because terms only exit the model.

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MULTIPLE

Specifies that multiple terms can enter the model at each step. So a higher order term can enter

the model, and the terms that comprise the term enter the model at the same time. For example,

if A*B is the most statistically significant term, A*B enters the model. At the same time, A and B

enter the model if those terms are not in the model already.

BACKWARDS does not use SINGLE or MULTIPLE because terms only exit the model.

GRSQUARE

After FVALIDATION, displays a graph of the R

statistic and the test deviance R

statistic for each step

in the model selection procedure.

Validation

TEST C K

Specifies to perform validation with a test data set that you specify. The column must have two distinct values

that indicate which rows are in the test data set and which rows are in the training data set. K is the value for rows

that are in the test data set.

TESTK [K]

Specifies to perform validation with a randomly-selected test data set. The first K is the fraction of the data in the

test data set, a value between 0 and 1. The second K is optional. The second K is a base for the random number

generator. If you specify the base, you can get the same test data set again by specifying the same base.

CVTEST C

Specifies to perform k-fold cross validation with a test data set that you specify. Rows with the same value in the

column are in the same fold.

CVTEST K [K]

Specifies to perform k-fold cross validation with randomly-selected folds. The constant is the number of folds.

The optional constant is a base for the random number generator. If you specify the base, you can get the same

folds again by specifying the same base.

Tables for all models

TMETHOD

Displays a table of information about the method and options.

TMSDETAILS

Displays the type of stepwise procedure and the alpha values to enter and/or remove a predictor from the model.

If you do not specify FULL or NOFULL, then Minitab uses the preferences that are set in File > Options > Linear

Models > Stepwise.

FULL

Specifies to display the coefficients, p-values, and model summary statistics for each step of the procedure.

If you use the TEST subcommand, the model summary statistics include test R

NOFULL

Hides these statistics.

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TEQUATION

Displays the model equation.

SINGLE

SINGLE specifies to show one equation instead of a separate equation for each combination of the levels of

the categorical predictors. The equation has a coefficient for each level of the categorical variables.

SEPARATE

Specifies to show separate equations for each combination of the levels of the categorical predictors. Each

equation has a different constant term.

TDEVIANCE

Displays the ANOVA table for the Wald test or the likelihood ratio tests.

TSUMMARY

Displays the statistics that evaluate model fit, including deviance R

TCOEFFICIENTS

Displays the coefficients and the p-values for the Wald normal approximation tests.

If you do not specify FULL or NOFULL, Minitab uses the preferences set in File > Options > Linear Models >

Display of Results.

FULL

Specifies to show the coefficients for the reference level when you use (0, 1) coding for categorical predictors.

NOFULL

Hides these statistics.

TGOODNESS

Displays the Pearson and Deviance goodness-of-fit tests. For the binomial distribution models, also shows the

Hosmer-Lemeshow goodness-of-fit test.

TDIAGNOSTICS [K]

Displays a table of diagnostics. K = 0 displays diagnostics for only observations with high leverage values or

standardized residuals greater than 2. K = 1 displays diagnostics for all observations.

TSTEP

Displays the deviance at each iteration of the coefficients.

TEXPAND

Displays the expanded versions of the tables that have them. The expanded Analysis of Variance table adds the

sequential deviances and percent contributions for each term. The Coefficients table adds confidence intervals

for the coefficients. The Fits and Diagnostics table adds the standard error of the fit, the 95% confidence interval

for the fit, the deleted residual, the HI, the Cook's D, and the DFITS.

TSIMPLE

Displays the simple versions of the Analysis of Variance table, the Coefficients table, and the Fits and Diagnostics

table.

NODEFAULT

Hides default tables and graphs that you do not include the subcommand to display.

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Tables for binomial distribution models

TRINFO

Displays the number of events, non-events, and the total. The table also indicates which response value is the

reference event.

TODDS

Displays odds ratios for each continuous predictor and each level of the categorical predictors. The output does

not show odds ratios for predictors with interactions in the model or for categorical predictors with (−1, 0,+1)

coding.

INCREMENT K...K

Specifies the increment for the odds ratio for the predictor with K. For example, if the predictor is mass in

grams, enter 1000 to see the change in the odds ratio for a kilogram. Enter the values in the same order that

the predictors follow CONTINUOUS. Enter 1 for a predictor to use the units of the data.

THOSMER

Displays the table of observed and expected values for the Hosmer-Lemeshow test.

TASSOCIATION

Displays the number of concordant and discordant pairs, the Somers' D, the Goodman-Kruskal Gamma, and

Kendall's Tau-a.

Graphs for all models

RTYPE K

Specifies the type of residual to plot with the graph subcommands.

Type of residualsValue of K

Regular or raw residuals (RESIDUALS)1 (default)

Standardized residuals (SRESIDUALS)2

Deleted Studentized residuals (TRESIDUALS)3

GHISTOGRAM

Displays a histogram of the residuals.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Displays residuals versus fitted values. For binary logistic regression, this plot is not available when the data are

in response/frequency format because the pattern that appears is not a violation of the assumptions.

GORDER

Displays residuals versus the order of the worksheet. The row number for each data point is shown on the x-axis

(for example, 1 2 3 4...n).

GFOURPACK

Choose to display a layout of the residual plots. For Poisson models, the layout includes a histogram of residuals,

a normal plot of residuals, a plot of residuals versus fits, and a plot of residuals versus order.

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For the binary logistic model, the layout depends on the format of the data. If the data are in response/frequency

format, the layout has a histogram of residuals, a normal plot of residuals, and a plot of residuals versus order. If

the data are in event/trial format, the layout also has a plot of residuals versus fits.

GVARIABLE C...C

Displays the residuals versus the variables in C. Typically, you enter the columns for the predictors.

Graphs for binomial distribution models

GROC

Displays the receiver operating characteristic (ROC) curve for a binomial logistic regression. A footnote on the

plot gives the area under the ROC curve. You can use the area under the ROC curve to compare models. The ROC

curve plots the true positive rate (TPR) against the false positive rate (FPR).

Storage for all models

When you use the TEST subcommand, Minitab Statistical Software stores the following statistics for the training data

set and not the test data set:

StatisticSubcommand

Deleted residualsTRESIDUALS

Cook's distancesCOOK

DFITSDFITS

Adjusted response valuesSADJUSTEDRESP

X'WX inverse matrix of the estimated coefficientsXPWXINVERSE

FITS C

Stores the event probabilities.

RESIDUALS C

Stores the Deviance residuals by default. Stores the Pearson residuals if you use the subcommand PEARSON.

SRESIDUALS C

Stores the standardized Deviance by default. Stores the standardized Pearson residuals if you use the subcommand

PEARSON.

TRESIDUALS C

Stores the deleted Deviance residuals by default. Stores the deleted Pearson residuals if you use the subcommand

PEARSON.

COOK C

Stores the Cook's distances. If you use a test data set, the storage is for the training data set only.

DFITS C

Stores the DFITS.

HI C

Stores the leverages.

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SADJUSTEDRESP C

Stores the final adjusted response values.

SIWEIGHTS C

Stores the internal weights used to estimate the model parameters.

SECOEFFICIENTS C

Stores the standard errors of the estimated coefficients.

COEFFICIENTS C

Stores the estimated coefficients.

XMATRIX M

Stores the design matrix.

XPWXINVERSE M

Stores the X'WX inverse matrix of the estimated coefficients.

When you use the TEST subcommand or the CVTEST subcommand, Minitab Statistical Software stores the following

statistics for the training data set but not for the validation data set.

DBETA C

Stores the change in the estimated regression coefficients (delta b) when you delete all observations with a

particular factor/covariate pattern. Use the delta b values to detect observations with a strong influence on the

coefficients. If you use a test data set, the storage is for the training data set only.

DSBETA C

Stores the change in the estimated regression coefficients when you delete all observations with a particular

factor/covariate pattern based on the standardized Pearson residual. If you use a test data set, the storage is for

the training data set only.

DCHISQUARE C

Stores the change in the chi-square statistic when you delete all observations with a particular factor/covariate

pattern. Observations that are poorly fit by the model have high delta chi-square values. If you use a test data

set, the storage is for the training data set only.

DDEVIANCE C

Stores the change in the deviance when you delete all observations with a particular factor/covariate pattern. If

you use a test data set, the storage is for the training data set only.

Storage for validation

Use the following commands to store statistics for validation with a test data set. These commands are sub-sub

commands to the TEST subcommand. As such, these commands must come after TEST.

Use the following commands to store statistics for validation with a test data set or validation with k-fold cross validation.

SSAMPLE C

When CVTEST is used:

•

The storage column, Sample_Id, stores the fold identification in C.

•

C contains values 1, 2, ..., K.

When TEST is used:

•

The storage column, Sample_Id, stores the training and test sample assignments in C.

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•

C is a binary text column that contains Training and Test as the two values.

CVFIT C

Stores the fitted values from each fold's turn as the test data set.

CVRESID C

Stores the residuals from each fold's turn as the test data set.

BFIT: Session command for creating a binary fitted

line plot

BFIT

Performs logistic regression on a binary response variable. A binary variable has only two possible values, such

as presence or absence. BFIT fits a model with one continuous predictor using an iterative-reweighted least squares

algorithm to obtain maximum likelihood estimates of the parameters.

For examples and information on entering the response data, go to Entering data for response variables on page

1142.

BFIT provides diagnostic plots, goodness-of-fit tests, and other diagnostic measures so you can assess the validity

of your model. For more information, go to Generalized linear model diagnostics and residual analysis on page

1177.

Note Minitab excludes all observations with missing values on either the response variable or any of the predictors from all calculations.

RESPONSE C [C]

Enters the response data as counts. For examples and information on entering the response data, go to

Entering data for response variables on page 1142.

CONTINUOUS C

Indicates the continuous predictor is in C.

LOGIT

Use the logit link function.

NORMIT

Use the normit link function.

GOMPIT

Use the gompit link function. The gompit link function is also called the complementary log-log link function.

Options

FREQUENCY C

Indicates how often the data in a row occur.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

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ITYPE K

Specifies the type of confidence interval.

Type of confidence intervalValue of K

Lower bound–1

Two-sided0 (default)

Upper bound1

REVENT K

Specifies which is the response event with K. The value of K matches one of the values in the response column.

Enclose text values in quotation marks. For example, to model the probability of a "Fail" in the response column,

enter REVENT "Fail".

EVNAME K

When the RESPONSE command has two columns, you can specify the name of the event with K. Enclose text

values in quotation marks.

Tables

TMETHOD

Displays the link function.

TRINFO

Displays the number of events, non-events, and the total.

TDEVIANCE

Displays the ANOVA table.

TSUMMARY

Displays the statistics that evaluate model fit, including deviance R

TCOEFFICIENTS

Displays the coefficients and the p-values for the Wald normal approximation tests.

TODDS

Displays odds ratios for each continuous predictor and each level of the categorical predictors. The output does

not show odds ratios for predictors with interactions in the model or for categorical predictors with (−1, 0, +1)

coding.

INCREMENT K

Specifies the increment for the odds ratio for the predictor with K. For example, if the predictor is mass in

grams, enter 1000 to see the change in the odds ratio for a kilogram.

TEQUATION

Displays the model equation.

TGOODNESS

Displays the deviance, Pearson, and Hosmer-Lemeshow tests. These tests assess the overall fit of the model.

TDIAGNOSTICS K

Displays a table of diagnostics. K = 0 displays diagnostics for only observations with high leverage values or

standardized residuals greater than 2. K = 1 displays diagnostics for all observations.

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NODEFAULT

Hides default tables and graphs that you do not include the subcommand to display.

Graphs

GFLINE

Displays the fitted line plot. The plot also shows the equation for the probabilities.

PROPORTIONS

Shows the data on the plot.

DCONFIDENCE

Shows the confidence interval for the fitted line on the plot.

RTYPE K

Specifies the type of residual to plot with the graph subcommands.

Type of residualsValue of K

Regular or raw residuals (RESIDUALS)1 (default)

Standardized residuals (SRESIDUALS)2

Deleted Studentized residuals (TRESIDUALS)3

GHISTOGRAM

Displays a histogram of the residuals.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Displays residuals versus fitted values. This plot is not available when the data are in response/frequency format

because the pattern that appears is not a violation of the assumptions.

GORDER

Displays residuals versus the order of the worksheet. The row number for each data point is shown on the x-axis

(for example, 1 2 3 4... n).

GFOURPACK

Displays a layout of the residual plots. If the data are in response/frequency format, the layout has a histogram

of residuals, a normal plot of residuals, and a plot of residuals versus order. If the data are in event/trial format,

the layout also has a plot of residuals versus fits.

GVARIABLE C...C

Displays the residuals versus the variables. Typically, you enter the columns for the predictors.

Storage

FITS C

Stores the event probabilities.

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RESIDUALS C

Stores the Deviance residuals.

SRESIDUALS C

Stores the standardized Deviance residuals.

TRESIDUALS C

Stores the deleted Deviance residuals.

COEFFICIENTS C

Stores the estimated coefficients.

OLOGISTIC: Session command for performing

ordinal logistic regression

OLOGISTIC C = C...C

Performs logistic regression on an ordinal response variable. Ordinal variables are categorical variables that have

3 or more possible levels with a natural ordering, such as strongly disagree, disagree, neutral, agree, and strongly

agree.

OLOGISTIC fits a model with one or more predictors using an iterative-reweighted least squares algorithm to

obtain maximum likelihood estimates of the parameters. OLOGISTIC assumes parallel lines; therefore, one set of

coefficients is associated with the predictors. When this assumption is not valid, use NLOGISTIC on page 216, which

generates separate logit functions.

The predictors can be either factors (nominal variables) or covariates (continuous variables). Factors can be crossed

or nested. Covariates can be crossed with each other or with factors, or nested within factors. The model can

include up to 9 factors and 50 covariates. Unless you specify a predictor in the model as a factor, Minitab assumes

the predictor is a covariate. Model continuous predictors as covariates and categorical predictors as factors. For

more information, go to How to specify the model for ATCLASS, GZLM, OLOGISTIC and NLOGISTIC on page 1165.

Factor columns can be numeric or text. For a numeric factor, Minitab designates the lowest numeric value as the

reference level. For a text factor, Minitab determines the reference level alphabetically. For an example, go to

Entering data for factor variables on page 1142. To change the default coding, use the REFERENCE subcommand.

When the response variable column is numeric, OLOGISTIC orders the levels from smallest to largest. When the

response variable column is text, OLOGISTIC orders the levels alphabetically. For examples and information on

entering the response data, go to Entering data for response variables on page 1142. Use the ORDER subcommand

to specify the ordering of the response variable.

FREQUENCY C

Enters the response data as counts. For examples, go to Entering data for response variables on page 1142.

Options

LOGIT

Specifies the logit link function (default).

NORMIT

Specifies the normit link function.

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GOMPIT

Specifies the gompit link function (also called the complementary log-log function).

FACTORS C...C

Specifies which of the predictors are factors. Minitab assumes all variables in the model are covariates unless

specified to be factors. Model continuous predictors as covariates and categorical predictors as factors.

REFERENCE C K, ..., C K

Changes the default coding for factors. To change the default reference factor level, specify the factor column

followed by the reference level. (Text and date/time levels must be enclosed in quotes.) For a discussion and

examples of the default coding scheme, go to Entering data for factor variables on page 1142.

Use the ORDER subcommand to specify the ordering of the response variable.

ORDER C

ORDER K...K

Specifies the order of the response values from lowest to highest. Values can be stored in C or K.

ITERATION K

Changes the maximum number of iterations that Minitab performs to reach convergence. The default value is 20.

Minitab's logistic regression commands obtain maximum likelihood estimates through an iterative process. If the

maximum number of iterations is reached before convergence, the command terminates.

START C

Specifies the initial values for model parameters. The column containing the initial values must have one row for

each estimated coefficient in the model. For OLOGISTIC, the starting values for all the constants appear first,

followed by starting values for the predictors in the model.

Note Each degree of freedom must be in one row.

CTOLERANCE K [K]

Changes the convergence criteria. Both Ks must be positive numbers.

How convergence is attainedK

Convergence is attained relative to this criterion if from one iteration to the next the change

in the log-likelihood is less than K. The default is .0001.

First K

Convergence is attained relative to this criterion if from one iteration to the next the

maximum change in the coefficients is less than K. The default is .0001.

Second K

TOLERANCE K

Specifies the tolerance level to keep a predictor in the model that is either highly correlated with another predictor

or nearly constant. By default, K = 4 * 2.22E–012. K must be a positive number. Lowering tolerance by giving a

very small argument can prevent Minitab from eliminating problematic predictor columns from the model.

Correlated predictors

1 – R-squared, where R-squared is the value resulting from regressing one predictor on the remaining

predictors. If (1 – R-squared) is < K, Minitab removes the predictor from the equation.

Constant predictors

The parameter for forcing in a variable that is nearly constant. If the coefficient of variation of a predictor

(the standard deviation / the mean) is < square root K, Minitab considers the predictor to be essentially

constant and removes it from the equation.

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Results

BRIEF K

Controls the amount of output with a value in K.

Output that is displayedValue of K

Minitab displays no output, but performs all specified storage and displays the following

error messages, warnings, prompts, and notes; graphs; WRITE to the screen.

Minitab displays the response information, logistic regression table, log-likelihood, and test

that all slopes = 0.

In addition to the BRIEF 1 output, displays the Pearson and deviance tests.2 (default)

In addition to the BRIEF 2 output, displays factor level values and tests for terms with more

than 1 degree of freedom.

STEP

Displays the log-likelihood at each iteration of the parameter estimation process.

Storage of the characteristics of the estimated equation

COEFFICIENTS C

Estimated coefficients in a column.

SECOEFFICIENTS C

Standard errors of the coefficients in a column.

XPWXINVERSE M

Variance-covariance matrix of the estimated coefficients, expressed as (X' W X)

−1

LOGLIKELIHOOD K

Last log-likelihood value.

Storage of the event probabilities and aggregated data

EPROBABILITY C...C

Predicted event probabilities. Specify one column for each distinct value of the response.

CUMPROBABILITY C...C

Cumulative event probabilities. Specify one column for each distinct value of the response minus one.

NTRIALS C

Number of trials for each factor/covariate pattern.

NOCCUR C...C

Number of occurrences for the j

factor/covariate pattern. Specify one column for each distinct value of the

response.

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NLOGISTIC: Session command for performing

nominal logistic regression

NLOGISTIC C = C…C

Performs logistic regression on a nominal response variable using an iterative-reweighted least squares algorithm

to obtain maximum likelihood estimates of the parameters. Nominal variables are categorical variables that have

3 or more possible levels but with no natural ordering. For example, the levels in a food-tasting study may include

crunchy, mushy, and crispy.

OLOGISTIC on page 213 assumes parallel lines. Therefore, one set of coefficients is associated with the predictors.

When this assumption is not valid, use NLOGISTIC, which generates separate logit functions.

The predictors can be either factors (nominal variables) or covariates (continuous variables). Factors can be crossed

or nested. Covariates can be crossed with each other or with factors, or nested within factors. The model can

include up to 9 factors and 50 covariates. Unless you specify a predictor in the model as a factor using the FACTORS

subcommand, Minitab assumes the predictor is a covariate. Model continuous predictors as covariates and

categorical predictors as factors. For more information, go to How to specify the model for ATCLASS, GZLM,

OLOGISTIC and NLOGISTIC on page 1165.

Factor columns can be numeric or text. For a numeric factor, Minitab designates the lowest numeric value as the

reference level. For a text factor, Minitab determines the reference level alphabetically. For an example, go to

Entering data for factor variables on page 1142.

When the response variable column is numeric, NLOGISTIC defines the highest value as the reference event. When

the response variable column is text, NLOGISTIC defines the event by reverse alphabetical order. For example, if

you enter the response variable as crunchy, mushy, and crispy, NLOGISTIC defines mushy as the event. For examples

and information on entering the response data, go to Entering data for response variables on page 1142.

Use the REFERENCE subcommand to change the default coding for both factor and response variables.

NLOGISTIC provides goodness-of-fit tests and other diagnostic measures so you can assess the validity of your

model.

FREQUENCY C

Enters the response data as counts. For examples and information on entering the response data, go to

Entering data for response variables on page 1142.

Options

FACTORS C...C

Specifies which of the predictors are factors. Minitab assumes all variables in the model are covariates unless

specified to be factors. Continuous predictors must be modeled as covariates; categorical predictors must be

modeled as factors.

REFERENCE C K, ..., C K

Changes the default coding for factor and response columns. To change the default reference factor level, specify

the factor column followed by the reference level. (Text and date/time levels must be enclosed in quotes.) For a

discussion and examples of the default coding scheme, go to Entering data for factor variables on page 1142.

To change the default event, specify the response column followed by the event value.

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START C

Specifies the initial values for model parameters. The column containing the initial values must have one row for

each estimated coefficient in the model. For NLOGISTIC, the starting values for logit 1 appear before logit 2, and

so on. For each logit, the starting value for the constant appears before the starting values for the predictors in

the model.

Note Each degree of freedom must be in one row.

ITERATION K

Changes the maximum number of iterations that Minitab performs to reach convergence. The default value is 20.

Minitab's logistic regression commands obtain maximum likelihood estimates through an iterative process. If the

maximum number of iterations is reached before convergence, the command terminates.

CTOLERANCE K K

Changes the convergence criteria. Both Ks must be positive numbers.

How convergence is attainedK

Convergence is attained relative to this criterion if from one iteration to the next the change

in the log-likelihood is less than K. The default is .0001.

First K

Convergence is attained relative to this criterion if from one iteration to the next the

maximum change in the coefficients is less than K. The default is .0001.

Second K

TOLERANCE K

Specifies the tolerance level to keep a predictor in the model that is either highly correlated with another predictor

or nearly constant. By default, K = 4 * 2.22E–012. K must be a positive number. Lowering tolerance by giving a

very small argument can prevent Minitab from eliminating problematic predictor columns from the model.

Correlated predictors

1 – R-squared, where R-squared is the value resulting from regressing one predictor on the remaining

predictors. If (1 – R-squared) is < K, Minitab removes the predictor from the equation.

Constant predictors

The parameter for forcing in a variable that is nearly constant. If the coefficient of variation of a predictor

(the standard deviation / the mean) is < square root K, Minitab considers the predictor to be essentially

constant and removes it from the equation.

Results

BRIEF K

Controls the amount of output with a value in K.

Output that is displayedValue of K

Minitab displays no output, but performs all specified storage and displays the following

error messages, warnings, prompts, and notes; graphs; WRITE to the screen.

Minitab displays the response information, logistic regression table, log-likelihood, and test

that all slopes = 0.

In addition to the BRIEF 1 output, displays the Pearson and deviance tests.2 (default)

In addition to the BRIEF 2 output, displays factor level values and tests for terms with more

than 1 degree of freedom.

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STEP

Displays the log-likelihood at each iteration of the parameter estimation process.

Storage of the characteristics of the estimated equation

COEFFICIENTS C

Estimated coefficients in a column.

SECOEFFICIENTS C

Standard errors of the coefficients in a column.

XPWXINVERSE M

Variance-covariance matrix of the estimated coefficients, expressed as (X' W X)

−1

LOGLIKELIHOOD K

Last log-likelihood value.

Storage of the event probabilities and aggregated data

EPROBABILITY C...C

Predicted event probabilities. Specify one column for each distinct value of the response.

NTRIALS C

Number of trials for each factor/covariate pattern.

NOCCUR C...C

Number of occurrences for the j

factor/covariate pattern. Specify one column for each distinct value of the

response.

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ANOVA

ONEWAY: Session command for performing a

one-way ANOVA

ONEWAY

Performs a one-way analysis of variance. Your data can be arranged in one of two ways:

•

Response data are in one column for all factor levels

•

Response data are in a separate column for each factor level

Minitab performs the traditional one-way ANOVA procedure if you assume that the variance is constant across

all groups. However, if your data exhibit unequal variances between groups, Minitab can perform Welch's ANOVA.

To perform Welch's ANOVA, use the WELCH subcommand.

Use the comparison method subcommands to determine which group means differ and by how much.

The factor column may be numeric or text, and may contain any value. The levels do not need to be in any special

order. SET is especially useful for entering data that follow a pattern, such as subscripts (factor level values). For

examples, go to Entering patterned data for the SET session command on page 1143. (TSET and DSET can be used

to enter text and date/time data, respectively, that follow a pattern.)

Variables and terms to include in the model

Specifies information about the variables and terms to include in the model. How you use these commands depends

on how your data are arranged in the worksheet. Your response data can either be in one column for all factor levels

or the response data can be in a separate column for each factor level.

RESPONSE C

RESPONSE C...C

Specifies the column or columns that contains the response data. The column or columns must be numeric or

date/time. If your response data are in one column, specify the column with C. If your response data are in a

separate column for each factor level, specify all of the response columns with C...C.

CATEGORICAL C

Required when your response data are in one column for all factor levels. For this data arrangement, you must

use CATEGORICAL to specify the single column that contains the categorical factor. If you specify more than one

column for RESPONSE, CATEGORICAL is not allowed.

Options

CONFIDENCE K

Specifies a confidence level for the interval plot and means table, but not the multiple comparisons, in K. For

example, for a 90% confidence level, enter CONFIDENCE 90. The default value of K is 95.

ITYPE K

Specifies the type of confidence interval for the interval plot and means table.

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Type of confidence intervalValue of K

Lower bound–1

Two-sided0 (default)

Upper bound1

WELCH

Specifies the Welch method. Use WELCH when you do not assume that all groups have equal variances. One-way

ANOVA with equal variances is slightly more powerful than Welch's ANOVA, but serious error can result if the

variances are not equal.

Comparisons

TUKEY [K]

Calculates all pairwise differences between level means using Tukey's method (also called Tukey's HSD or

Tukey-Kramer method) with the family error rate specified as K. If K is not specified, Minitab uses the default

family error rate of .05.

To obtain output for this comparison method, you must issue at least one of the following subcommands:

TGROUPING, TMTEST, GMCI.

FISHER [K]

Calculates all pairwise differences between level means using Fisher's LSD procedure with the individual error rate

specified as K. If K is not specified, Minitab uses the default individual error rate of .05.

To obtain output for this comparison method, you must issue at least one of the following subcommands:

TGROUPING, TMTEST, GMCI.

DUNNETT [K] K

DUNNETT C

Calculates the difference between each treatment mean and a control mean. The family error rate is specified by

the first K. How you specify the control group depends on how your data are arranged.

Specify the control group as follows:

•

If your response data are in one column, specify the control group with the second K.

•

If your response data are in a separate column for each factor level, specify the control group column with C.

If the error rate is not specified, Minitab uses the default family error rate of .05.

When the column of levels contains text data, the control level will also be text so remember to enclose the value

you enter for K in double quotes.

To obtain output for this comparison method, you must issue at least one of the following subcommands:

TGROUPING, TMTEST, GMCI.

MCB [K] K

Calculates the difference between each level mean and the best of the other level means. The family error rate is

specified by the first K and the type of best is specified by the second K. If the error rate is not specified, Minitab

uses the default family error rate of .05.

There are two choices for "best." If the smallest mean is considered the best, set K = -1; if the largest is considered

the best, set K = 1.

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To obtain output for this comparison method, you must issue at least one of the following subcommands:

TGROUPING, TMTEST, GMCI.

GAMES [K]

Calculates all pairwise differences between level means using Games-Howell method with the family error rate

specified as K. If K is not specified, Minitab uses the default family error rate of .05. GAMES is only available when

WELCH is issued.

To obtain output for this comparison method, you must issue at least one of the following subcommands:

TGROUPING, TMTEST, GMCI.

Results

NODEFAULT

Specifies that no default tables and graphs will be displayed.

TEXPAND

Displays the expanded versions of the ANOVA table, Model summary, and Multiple comparisons tables.

TSIMPLE

Displays the simple version of all tables.

TMETHOD

Displays the method table.

TFACTOR

Displays the name, number of levels, and the values for the categorical factor.

TANOVA

Displays the ANOVA table.

TSUMMARY

Displays the summary of model table.

TMEANS

Displays the table of group means.

TGROUPING

Displays the grouping information table for each comparison method subcommand that you issue. This table

highlights the significant and non-significant comparisons for each comparison method.

TMTEST

Displays the multiple comparison test table for each comparison method subcommand that you issue. This table

displays the hypothesis test form of the comparison output, which includes the differences of the means, the

numeric values for the confidences intervals, and the adjusted p-values.

Graphs that display information by group

GINTPLOT

Displays an interval plot of the group means.

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GINDPLOT

Displays an individual value plot for each group.

GBOXPLOT

Displays a boxplot for each sample.

Graphs for the residuals

GHISTOGRAM

Displays a histogram of the residuals.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Plots the residuals versus the fitted values.

GORDER

Plots the residuals versus the order of the data. The row number for each data point is shown on the x-axis ( for

example, 1 2 3 4... n).

GFOURPACK

Displays a layout of a histogram of the residuals, a normal probability plot of the residuals, residuals versus fitted

values, and residuals versus order of the data.

GVARIABLE C...C

Displays a separate graph for the residuals versus each specified column.

Other graphs

GMCI

Displays an interval plot of the differences between group means for each comparison method subcommand that

you issue.

Storage

FITS C

FITS C...C

Stores fitted values. The fitted values are the level means. Specify one storage column C for each column that

contains response data.

RESIDUALS C

RESIDUALS C...C

Stores residuals. Residual = (response – fit). Specify one storage column C for each column that contains response

data.

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ANOM: Session command for creating an analysis

of means chart

ANOM C

Displays an analysis of means (ANOM) chart. You must choose one distribution that the response data follows:

normal (one-way or two-way), binomial, or Poisson.

For ANOM with a normal distribution and one factor, a single plot displaying the means for each level of the

factor, a center line which is the grand mean, and upper and lower decision limits is produced. If you use ANOM

with a normal distribution and two factors, three plots are given: one showing the interaction effects, one showing

the main effects for the first factor, and one showing the main effects for the second factor. If you choose an

ANOM for a binomial distribution or a Poisson distribution, a single plot displaying the means for each level of

the factor, a center line which is the grand mean, and upper and lower decision points is produced.

The subcommands NORMAL, BINOMIAL, and POISSON replace the commands %ANOM, %BANOM, and %PANOM.

respectively.

NORMAL C [C]

For normal data, each row of the response column represents an observation for measurement data. Response

data are typically measurement data, such as weight or moisture content.

BINOMIAL K

For binomial data, the values in the response column are the numbers of defectives found in each sample.

These values must be positive integers (0 or greater). You can include up to 500 samples.

POISSON

For Poisson data, the values in the response column are the numbers of defects that are found in each sample.

These values must be positive integers (0 or greater). You can include up to 500 samples.

ALPHA K

The decision lines on an ANOM chart are based on an experiment-wide error rate, similar to what you might

use when making pairwise comparisons or contrasts in an ANOVA. By default, a rate of 0.05 is used. Use

ALPHA to enter a number between 0 and 1. Values greater than or equal to 1.0 are interpreted as percentages.

TITLE "title"

Specifies a title for the graph. If you do not specify a title, Minitab uses a default title.

When you use TITLE with %graphs or the DENDROGRAM subcommand, you can use it only one time, and

you cannot use any of the TITLE subcommands available with the graph commands.

WTITLE "title"

You can use WTITLE as a subcommand with LAYOUT and all graphs. The title that you specify becomes the

command title of the resulting graph.

GSAVE "file_name"

GSAVE K

Saves the graph in a file.

The default file name is Minitab.PNG. You can specify a custom file name in double quotation marks

("file_name"), or as a stored text constant (K). You can also use any of the following subcommands to save

the graph in a different graphics format.

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Some graph commands—for example, HISTOGRAM C1 C2 C3—generate more than one graph. If you include

the GSAVE subcommand with such a command, Minitab saves multiple files. Minitab gives each file a different

file name. Minitab uses the first five characters of the name you specify, then appends a number (001, 002,

and so on), for up to 300 files.

JPEG

JPEG color

PNGB

PNG grayscale

PNGC

PNG color

TIFB

TIF grayscale

TIF

TIF color

BMPB

BMP grayscale

BMPC

BMP color

GIF

EMF

RESOLUTION K

Saves the graph at a resolution of K dots per inch.

ANOVA: Session command for performing a

balanced ANOVA

ANOVA C...C

Performs univariate and multivariate analysis of variance.

For one-way analysis of variance, you can have unbalanced designs. For multi-way analysis of variance, you must

have balanced designs (all cells must have the same number of observations). The command GLM on page 227

analyzes balanced and unbalanced designs. However, if your design is balanced, ANOVA is faster and requires

less space.

Factors can be crossed or nested, fixed or random. ANOVA calculates all exact F-tests, displays expected mean

squares, and estimates variance components. You can specify your own tests, store residuals and fitted values,

and display cell and marginal means. You can analyze up to 50 response variables and up to 9 factors on one

ANOVA command.

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Minitab verifies that your model is valid and displays a message if it is not. Minitab also verifies that your data set

is balanced.

Note Balanced data are not required for one-factor models.

If you do not use the subcommand RANDOM, ANOVA fits a fixed effect model. In this case, the F-statistic for a

term is always (MS term) / (MSE). However, when some of your factors are random, the denominator of an F-test,

in general, is not MSE. For more information, see the subcommands RANDOM and EMS.

You can use the subcommands MANOVA, SSCP, EIGEN, PARTIAL, and NOUNIVARIATE to do multivariate analysis

of variance. Hotelling's T-squared test can be performed as a special case of ANOVA with MANOVA.

ANOVA can analyze very complex designs, but, if you have a simple model, ANOVA is very easy to use.

Fore more information, go to How to specify the model in ANOVA on page 1169 and How to enter data for ANOVA

and GLM on page 1158.

Model

RANDOM C...C

Specifies which factors are random. Do not include interaction terms or nested factors. If at least one factor in an

interaction term is random, then Minitab considers the term to be random. Any term that is nested within a

random factor is considered random.

RESTRICT

There are two mixed model analysis of variance models: one requires the crossed, mixed terms to sum to 0 over

subscripts that correspond to fixed effects (Minitab refers to this as the restricted model.), and the other does

not. Most textbooks and BMDP's program 8V use the restricted model. SAS uses the unrestricted model.

By default, ANOVA fits the unrestricted model. This subcommand instructs the program to fit the restricted model,

which assumes that the mixed interaction terms are restricted to sum to 0 over the fixed effects. For more

information, go to Restricted and unrestricted mixed models on page 1144.

Results

EMS

Displays a table that contains expected mean squares, estimated variance components, and the error term (the

denominator) used in each exact F-test. If no exact F-test for a term exists, then use the expected mean squares

to determine how to construct an approximate F-test using the subcommand TEST.

The estimates of the variance components are the usual unbiased analysis of variance estimates. They are obtained

by setting each calculated MS equal to its EMS. This gives a system of linear equations in the unknown variance

components. This system is then solved. Unfortunately, this method can result in negative estimates, which should

be set to 0. However, Minitab displays the negative estimates because they sometimes indicate that the model

that is being fit is inappropriate for the data.

Terms that are fixed do not have variance components estimated.

MEANS termlist

Displays a table of means corresponding to each term listed. For example, the following command language

displays four tables, one table for each main effect, A, B, D, and one for the three-way interaction, A*B*D.

ANOVA Y = A|B|C(A B) |D;

MEANS A B D A*B*D

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Minitab Statistical Software ANOVA

Terms listed on the MEANS statement must also be terms listed in the model. If you specify more than one MEANS

subcommand, then Minitab uses the last one. For more information, go to How to specify the model in ANOVA

on page 1169.

TEST termlist / errorterm

Calculates univariate F-tests. Termlist is a list of terms in the model. Each term is used as the numerator in a test.

Errorterm is a term in the model to be used as the denominator in all the test. Alternatively, errorterm can be a

linear combination of terms in the model and MSE (denoted by the reserved word ERROR). Minitab calculates the

F-ratios and p-values for you. Here are two examples.

TEST A/A*B + A*C + B*C - 2*A*B*C - 2*ERROR

TEST A B/A*B

Minitab constructs the synthetic denominator MS for the F-test. This denominator is the linear combination of

the MSs that are specified on TEST, and should have the same expectation as the numerator MS under the

hypothesis being tested. Suppose the denominator is the linear combination:

Then it has approximate degrees of freedom calculated by

where DF

is the degrees of freedom for the term MS

. For more information, go to Restricted and unrestricted

mixed models on page 1144.

Storage

FITS C...C

Stores fitted values, using one column for each response. If you specify a full model (you include all interaction

terms), then the fitted values are just the cell means. If you fit a reduced model, the fitted values are not the cell

means, but are the sum of the least squares estimates of the effects in the model.

RESIDUALS C...C

Stores residuals, using one column for each response variable. Residual = (response – fit).

Graphs

GHISTOGRAM

Displays a histogram of the residuals.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Plots the residuals versus the fitted values.

GORDER

Plots the residuals versus the order of the data. The row number for each data point is shown on the x-axis ( for

example, 1 2 3 4...n).

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GFOURPACK

Displays a layout of a histogram of the residuals, a normal probability plot of the residuals, residuals vs fitted

values, and residuals vs order of the data.

GVARIABLE C...C

Displays a separate graph for the residuals versus each specified column.

Balanced MANOVA

MANOVA [termlist [ / errorterm]]

Performs four multivariate tests—Wilks' test, Lawley-Hotelling test, Pillai's test, and Roy's largest root test—for

each term in the model. If you include a termlist, MANOVA performs the four multivariate tests for each term

listed. Hotelling's T-squared test can be performed as a special case of ANOVA with MANOVA.

If you specify an errorterm, then it must be a single term that is in the model. MANOVA then uses this errorterm

in all tests. If you do not specify an errorterm, then Minitab determines an appropriate errorterm, as in the univariate

case.

All four tests are based on two SSCP (sums of squares and cross products) matrices: H = the hypothesis matrix

and E = the error matrix. There is one H associated with each term in termlist. E is the matrix associated with the

error for the test.

SSCP

Displays the hypothesis matrix, H, that corresponds to each term that is specified by MANOVA, and the error

matrix, E.

EIGEN

Displays a table that contains the eigenvalues and eigenvectors for the (nonsymmetric) matrix, E-1 H. These are

the eigenvalues that are used to calculate the four MANOVA tests. A separate table is displayed for each term

that is specified on MANOVA.

Note If an eigenvalue is repeated, then the corresponding eigenvectors are not unique. In this case, the eigenvectors that Minitab

displays and those in books or other software might not agree. However, the MANOVA tests are always unique.

PARTIAL

Displays a matrix of partial correlations. These are the correlations among the residuals or, equivalently, the

correlations among the responses conditioned on the model. The formula for the matrix is W**-.5 E W**-.5, where

E is the error matrix and W has the diagonal of E as its diagonal and 0s elsewhere.

NOUNIVARIATE

Suppresses the univariate output. Only the multivariate output is displayed.

GLM: Session command for fitting the general linear

model

GLM

Fits the general linear model (GLM). Using GLM, you can perform analysis of variance with balanced and unbalanced

designs, analysis of covariance, and regression.

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Factors can be crossed or nested, fixed or random. Covariates can be crossed with each other or with factors, or

nested within factors. You can store residuals, fitted values, and many other diagnostics, and display cell and

marginal means.

GLM does tests for fixed effects models automatically. The TEST subcommand allows you to calculate the

appropriate univariate F-tests for mixed models.

Output contains both the sequential sums of squares and the adjusted sums of squares (that is, each term is fit

after all other terms in the model). Automatic tests are done using the adjusted SS, assuming all factors are fixed.

Observations that are considered unusual are displayed. You can also display coefficients, along with their standard

deviations and t-values, and cell and marginal means.

Calculations are performed using a regression approach. First a "full rank" design matrix is formed from the factors

and covariates. The columns of the design matrix are used as predictors. Then each response variable is regressed

on these columns.

You can store the residuals, fitted values, prediction and confidence intervals, and many other diagnostics for

further analysis.

For more information, go to How to specify the model for GLM on page 1167 and How to enter data for ANOVA

and GLM on page 1158.

Options

RESPONSE C

Specifies the column that contains the response variable. The column must be numeric or date/time.

CONTINUOUS C...C

Specifies the continuous predictors if you have any. The column or columns must be numeric or date/time and

must match the length of the response column.

CATEGORICAL C...C

Specifies the categorical predictors if you have any. The column or columns can be numeric, text, or date/time

and must match the length of the response column.

TERMS termlist

Specifies the model terms. Terms must be legal cross-terms. Only continuous predictors may be repeated. Nested

terms are not entered in the term list. The model can be nonhierarchical when there are no random factors.

RANDOM C...C

Specifies random factors. Random factors must also be specified in CATEGORICAL.

NESTED C (C...), ..., C (C...C)

Specifies the nesting relationships. Parentheses are used for nesting. For example, when B is nested within A, use

B (A), and when C is nested within both A and B, use C (A, B). Terms in parentheses are always factors in the model

and are listed with commas between them. Thus, D (A, B, E) is correct but D (A*B E) and D (A*B*E) are not. Also,

parentheses are not used inside parentheses. Thus, C (A, B) is correct but C (A, B (A)) is not.

WEIGHT

Performs a weighted regression. Weights cannot be used with optimal Box-Cox transformation. Column must be

numeric with nonnegative values. Length must match response column length.

An n x n matrix W is formed with the column of weights as its diagonal and zeros elsewhere. The regression

coefficients are estimated by:

( X' W X)

−1

(X' W Y)

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Minitab Statistical Software ANOVA

This is equivalent to minimizing the weighted SS Error:

Σ w

(Y – )

where w

is the weight in row i

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

ITYPE K

Specifies the type of confidence interval.

Type of confidence intervalValue of K

Lower bound–1

Two-sided0 (default)

Upper bound1

TOLER K

Specifies the tolerance level for collinearity and constant check. Use to force Minitab to keep a predictor in the

model which is either highly correlated with another predictor or nearly constant. The default k = 4*2.22E-16.

SSQUARES

Specifies sequential sum of squares for tests in the ANOVA table. The default is the adjusted sums of squares.

MEANS termlist

Calculates least squares means for specified terms. Terms must be in the model.

TEST termlist / error term

Use TEST to specify your own tests. Termlist is a list of terms in the model. List them exactly as on the TERMS

subcommand. Error term is the term to be used as the denominator for the F-test. This can be a term in the model,

a linear combination of terms in the model, or MSE (denoted by the word ERROR). Only available via command

line.

EFFECT

Specifies the effect coding (−1, 0, +1) scheme for categorical predictors. If you do not specify either EFFECT or

BINARY, then Minitab uses the preferences set in File > Options > Linear Models > Coding of Predictors.

BINARY

Specifies the binary coding (1, 0) scheme for categorical predictors. If you do not specify either EFFECT or BINARY,

then Minitab uses the preferences set in File > Options > Linear Models > Coding of Predictors.

REFERENCE C K...C K

Changes the default coding for the categorical predictor columns. To change the default reference factor level,

specify the factor column followed by the reference level. (You must enclose text and date/time levels in double

quotes.) You can assign a reference level only when you use the binary coding (1, 0) scheme.

Options for standardizing the continuous predictor

Use this set of subcommands to standardize the continuous predictors in your model. You can use SCALE and LOCATION

in conjunction with each other. LEVELS is mutually exclusive with the LOCATION and SCALE subcommands. If you do

not specify LOCATION, SCALE, LEVELS, or UNSTANDARDIZED, then Minitab uses the preferences set in File > Options

> Linear Models > Coding of Predictors.

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LOCATION [K...K]

Specifies that the analysis is to be performed on coded continuous predictors by subtracting a constant from

each predictor. If you do not specify any arguments, the mean of each predictor column is subtracted. K specifies

to subtract a constant. If you specify arguments, the number of arguments must match the number of continuous

predictors.

SCALE [K...K]

Specifies that the analysis is to be performed on coded continuous predictors by dividing each predictor by a

constant. If you do not specify any arguments, each predictor column is divided by the standard deviation. K

specifies to divide by a constant. If you specify arguments, the number of arguments must match the number of

continuous predictors.

LEVELS [K...K]

Specifies that the analysis is to be performed on coded continuous predictors by DOE-type coding for the specified

low and high levels K K…K K. The number of arguments must be twice the number of continuous predictors.

UNSTANDARDIZED

Specifies the analysis is to be performed on the original predictors.

Box-Cox

BOX-COX [K]

Performs a Box-Cox transformation with a specified lambda. K is the value of lambda and must be between −5

and +5. If K is not given, Minitab will find the optimal lambda. By default, Minitab rounds the optimal value.

Minitab cannot calculate the optimal lambda for stepwise regression or when the model contains random factors.

Consequently, you must specify a lambda value for BOXCOX if you use RANDOM, STEPWISE, FORWARD, or

BACKWARD.

Stepwise

FINFORMATION, STEPWISE, FORWARD, and BACKWARD perform a stepwise regression procedure to fit the model. No

arguments are needed for these subcommands.

FINFORMATION

Specifies a stepwise model selection procedure that uses forward information criteria selection. Use AICCORRECTED

or BICRITERION to specify which information criterion to use to select the final model. If you do not specify a

criterion, Minitab uses AICCORRECTED.

The forward information criteria procedure adds the term with the lowest p-value to the model at each step. If

you do not include subcommands about hierarchy, FINFORMATION adds 1 term at a step and maintains model

hierarchy, the equivalent of the following:

HIERARCHICAL; ALLTERMS; ALWAYS; SINGLE.

Minitab calculates the information criteria for each step.

In most cases, the procedure continues until one of the following conditions occurs:

•

The procedure does not find a new minimum of the criterion for 8 consecutive steps.

•

The procedure fits the full model.

•

The procedure fits a model that leaves 1 degree of freedom for error.

If you specify settings for the procedure that require a hierarchical model at each step and allow only one term

to enter at a time, then the procedure continues until it either fits the full model or fits a model that leaves 1

degree of freedom for error. Minitab displays the results of the analysis for the model with the minimum value

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of the selected information criterion, either the corrected Akaike's Information Criterion (AICc) or the Bayesian

Information Criterion (BIC).

AICCORRECTED

Specifies the use of the corrected Akaike's Information Criterion (AICc) to select the final model.

BICRITERION

Specifies the use of the Bayesian Information Criterion (BIC) to select the final model.

STEPWISE

Specifies a stepwise model selection procedure that uses both forward selection and backward elimination. If you

do not include subcommands about hierarchy, STEPWISE and FORWARD add 1 term at a step and maintain model

hierarchy, the equivalent of the following:

HIERARCHICAL; ALLTERMS; ALWAYS; SINGLE.

FORWARD

Specifies a stepwise model selection procedure that uses forward selection. If you do not include subcommands

about hierarchy, STEPWISE and FORWARD add 1 term at a step and maintain model hierarchy, the equivalent of

the following:

HIERARCHICAL; ALLTERMS; ALWAYS; SINGLE.

BACKWARD

Specifies a stepwise model selection procedure that uses backward elimination. Removes a single term at each

step and maintains a hierarchical model, the equivalent of the following:

HIERARCHICAL; ALLTERMS; ALWAYS.

AENTER K

Specifies the alpha level at which a term is entered into the model. The default is 0.15 for STEPWISE and 0.25

for FORWARD.

AREMOVE K

Specifies the alpha level at which a term is removed from the model. The default is 0.15 for STEPWISE and

0.10 for BACKWARD. For STEPWISE, K must be greater than or equal to K for AENTER.

ENTER termlist

Specifies the terms that are contained in the starting model for STEPWISE. The ENTER termlist must be a

subset of the TERMS termlist or in the default term list in the design.

FORCE termlist

Specifies the terms to be forced in the model. The FORCE termlist must be a subset of the TERMS termlist

or in the default term list in the design.

NOHIERARCHICAL

Specifies that the model selection procedure does not consider hierarchy.

HIERARCHICAL

Maintains a hierarchical model in stepwise regression. In a hierarchical model, if a higher-order term is

included, all lower-order terms that comprise the higher-order term also appear in the model. For example,

a model that includes the interaction term A*B*C is hierarchical if it includes the following main effects and

lower-order interactions: A, B, C, A*B, A*C, and B*C.

CATONLY

Specifies that only the categorical terms in the model have to be hierarchical.

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ALLTERMS

Specifies that both categorical and continuous terms have to be hierarchical.

ATEND

Specifies that the final step of the stepwise procedure adds terms to make the model hierarchical.

ALWAYS

Specifies that the model is hierarchical at every step.

SINGLE

Specifies that only one term can enter the model at each step. So a higher-order term can enter

the model only if the terms that comprise the term are already in the model. For example, the

algorithm does not consider the addition of A*B unless A and B are already in the model.

BACKWARDS does not use SINGLE or MULTIPLE because terms only exit the model.

MULTIPLE

Specifies that multiple terms can enter the model at each step. So a higher order term can enter

the model, and the terms that comprise the term enter the model at the same time. For example,

if A*B is the most statistically significant term, A*B enters the model. At the same time, A and B

enter the model if those terms are not in the model already.

BACKWARDS does not use SINGLE or MULTIPLE because terms only exit the model.

Graphs

RTYPE K

Specifies the type of residual to plot with the graph subcommands.

Type of residualsValue of K

Regular or raw residuals (RESIDUALS)1 (default)

Standardized residuals (SRESIDUALS)2

Deleted Studentized residuals (TRESIDUALS)3

GHISTOGRAM

Displays a histogram or individual value plot of the residuals, depending on the sample size.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Plots the residuals versus the fitted values.

GFOURPACK

Displays a layout of a histogram of the residuals, a normal probability plot of the residuals, residuals vs fitted

values, and residuals vs order of the data.

GORDER

Plots the residuals versus the order of the data. The row number for each data point is shown on the x-axis (for

example, 1 2 3 4...n).

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GVARIABLE C...C

Displays a separate graph for the residuals versus each specified column.

Results

NODEFAULT

Specifies that no default tables and graphs will be displayed.

TMETHOD

Displays the method table.

TFACTOR

Displays the name, number of levels, and the values for all categorical factors in your model.

TMSDETAILS

Displays the type of stepwise procedure and the alpha values to enter and/or remove a predictor from the model.

If you do not specify FULL or NOFULL, then Minitab uses the preferences set in File > Options > Linear Models

> Stepwise.

FULL

Displays the coefficients, p-values, Mallows' Cp, and model summary statistics for each step of the procedure.

NOFULL

Hides these statistics.

TEQUATION

Displays the regression equation table. Minitab will display up to 50 equations. If you do not specify SINGLE or

SEPARATE, Minitab uses the preferences set in File > Options > Linear Models > Display of Results.

SINGLE

If you want to view a single equation, rather than a separate equation for each factor level combination, use

SINGLE.

SEPARATE

To view the separate equations, use SEPARATE.

TCOEFFICIENTS

Displays the table of coefficients. If you do not specify FULL or NOFULL, Minitab uses the preferences set in File

> Options > Linear Models > Display of Results.

FULL

Displays the complete set of coefficients for categorical predictors.

NOFULL

Displays only the linearly independent coefficients.

TSUMMARY

Displays the summary of model table.

TANOVA

Displays the ANOVA table.

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TDIAGNOSTICS K

Displays a table of diagnostics. K = 0 displays diagnostics for only unusual observations. K = 1 displays diagnostics

for all observations.

TDW

Displays Durbin-Watson statistics.

TEXPAND

Displays the expanded version of the ANOVA table, table of coefficients, model summary table, and table of

unusual observations.

TSIMPLE

Displays the simple version of the ANOVA table, table of coefficients, model summary table, and table of unusual

observations.

TEMS

Displays the table of expected mean squares, estimated variance components, and error term in each F-test.

TMEANS

Displays the table of least squares means.

TVARIANCE

Displays the table of variance components.

TTEST

Displays the table of your own tests.

Storage for Box-Cox

BCRESP

Stores the Box-Cox transformation of the response in C.

BFITS

Stores the fits for the original response.

BSMEANS

Stores the means for the original response.

Storage of fits and residuals

RESIDUALS

Stores the residuals (fitted values – observed values).

SRESIDUALS

Stores the standardized residuals.

TRESIDUALS

Stores the deleted Studentized residuals.

Stores the leverages.

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COOK

Stores Cook's distance.

DFITS

Stores DFITS.

Storage of characteristics of the estimated equation

COEFFICIENTS

Stores the estimated coefficients.

FITS

Stores the fitted values, often called the Y-hats ( ).

SMEANS

Stores the least square means for the terms specified by MEANS.

XMATRIX

Stores the design matrix for regression model.

REML: Session command for fitting a mixed effects

model

REML

By default, fits a model with random factors with the Restricted Maximum Likelihood method (REML). Using REML,

you can perform analysis of variance on random factors with balanced or unbalanced designs. With the

subcommand MLE, you can use maximum likelihood estimation.

For REML, at least 1 factor must be random. Other factors can be fixed or random. Covariates can be crossed with

each other, with factors, or nested within factors. You can store residuals, fitted values, and other diagnostic

statistics.

REML tests for fixed effects with either the Kenward-Roger approximation or the Satterthwaite approximation.

To use the Satterthwaite approximation, use the subcommand SATTERTHWAITE. To show the tests, use the

subcommand TFIXEDEFFECT.

Tests use the adjusted sums of squares. Observations with standardized residuals that have absolute values greater

than 2 are in a table of unusual observations. to show the table, use the subcommand TDIAGNOSTICS.

REML has 4 required subcommands:

•

RESPONSE

•

RANDOM

•

CATEGORICAL

•

TERMS

For more information, go to the descriptions of the individual subcommands.

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Options

RESPONSE C

Specifies the column that contains the response variable. The column must be numeric or date/time.

CATEGORICAL C...C

Specifies the categorical predictors if you have any. The column or columns can be numeric, text, or date/time

and must match the length of the response column.

CONTINUOUS C...C

Specifies the continuous predictors if you have any. The column or columns must be numeric or date/time and

must match the length of the response column.

RANDOM C...C

Specifies random factors. Random factors must also be specified in CATEGORICAL. At least one factor must be

random.

NESTED C (C...), ..., C (C...C)

Specifies the nesting relationships. Parentheses are used for nesting. For example, when B is nested within A, use

B (A), and when C is nested within both A and B, use C (A, B). Terms in parentheses are always factors in the model

and are listed with commas between them. Thus, D (A, B, E) is correct but D (A*B E) and D (A*B*E) are not. Also,

parentheses are not used inside parentheses. Thus, C (A, B) is correct but C (A, B (A)) is not.

TERMS termlist

Specifies the model terms. Terms must be legal cross-terms. Only continuous predictors may be repeated. Nested

terms are not entered in the term list. The model can be nonhierarchical.

WEIGHT C

Performs a weighted regression. Weights cannot be used with optimal Box-Cox transformation. Column must be

numeric with nonnegative values. Length must match response column length.

An n x n matrix W is formed with the column of weights as its diagonal and zeros elsewhere. The regression

coefficients are estimated by:

( X' W X)

−1

(X' W Y)

This is equivalent to minimizing the weighted SS Error:

Σ w

(Y – )

where w

is the weight in row I

REML

Specifies to use the restricted maximum likelihood estimation method to estimate the variance components.

MLE

Specifies to use the maximum likelihood method to estimate the variance components. Usually, you use the REML

method

MAXITER K

Specifies the maximum number of iterations for the Newton-Raphson algorithm to estimate the variance

components.

STARTING C...C or K...K

Specifies the starting estimates for the variance components for each response in C. The order of the values in

each column must be the same order of the random terms in TERMS. The last value in C is for the error term. If

you have only one response or you want to use the same starting estimates for all responses, you can enter

numbers K...K.

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CTOLERANCE K

Specifies the convergence tolerance value in K for the objective function. The objective function is -2 log likelihood.

ETOLERANCE K

Specifies the convergence tolerance value in K for the estimates of the variance components.

KENWARDROGER

Use Kenward-Roger approximation to estimate the denominator degrees of freedom for the fixed effect tests.

SATTERTHWAITE

Use Satterthwaite approximation to estimate the denominator degrees of freedom for the fixed effects.

DROWSTAT

Computes statistics that require row deletion:

•

PRESS

•

R-Sq(Pred)

•

Cook's D

•

DFITS

•

Marginal deleted residuals

•

Marginal conditional residuals

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

ITYPE K

Specifies the type of confidence interval.

Type of confidence intervalValue of K

Lower bound−1

Two-sided0

Upper bound1

TOLERANCE K [K]

Specifies the tolerance level for the collinearity between variables and whether a variable is constant. The first K

specifies the tolerance for collinearity. The default value is 1E−18. The second K is for the tolerance for whether

a variable is constant. The default value is 1E−21. Lower the tolerance to keep variables in the model that Minitab

would exclude with the default values.

MEANS termlist

Calculates least squares means for specified terms. Terms must be in the model.

Options for standardizing the continuous predictor

Use this set of subcommands to standardize the continuous predictors in your model. You can use SCALE and LOCATION

in conjunction with each other. LEVELS is mutually exclusive with the LOCATION and SCALE subcommands. If you do

not specify LOCATION, SCALE, LEVELS, or UNSTANDARDIZED, then Minitab uses the preferences set in File > Options

> Linear Models > Coding of Predictors.

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LOCATION [K...K]

Specifies that the analysis is to be performed on coded continuous predictors by subtracting a constant from

each predictor. If you do not specify any arguments, the mean of each predictor column is subtracted. K specifies

to subtract a constant. If you specify arguments, the number of arguments must match the number of continuous

predictors.

SCALE [K...K]

Specifies that the analysis is to be performed on coded continuous predictors by dividing each predictor by a

constant. If you do not specify any arguments, each predictor column is divided by the standard deviation. K

specifies to divide by a constant. If you specify arguments, the number of arguments must match the number of

continuous predictors.

LEVELS [K...K]

Specifies that the analysis is to be performed on coded continuous predictors by DOE-type coding for the specified

low and high levels K K…K K. The number of arguments must be twice the number of continuous predictors.

UNSTANDARDIZED

Specifies the analysis is to be performed on the original predictors.

Graphs

RTYPE K

Specifies the type of residual to plot with the graph subcommands.

Type of residualsValue of K

Regular or raw residuals (RESIDUALS)1 (default)

Standardized residuals (SRESIDUALS)2

Deleted Studentized residuals (TRESIDUALS)3

CONDITIONAL

Specifies to plot residuals for conditional fits.

MARGINAL

Specifies to plot residuals for marginal fits.

GHISTOGRAM

Displays a histogram or individual value plot of the residuals, depending on the sample size.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Plots the residuals versus the fitted values.

GFOURPACK

Displays a layout of a histogram of the residuals, a normal probability plot of the residuals, residuals vs fitted

values, and residuals vs order of the data.

GORDER

Plots the residuals versus the order of the data. The row number for each data point is shown on the x-axis (for

example, 1 2 3 4...n).

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GVARIABLE C...C

Displays a separate graph for the residuals versus each specified column.

Tables

NODEFAULT

Specifies that no default tables and graphs will be displayed.

TEXPAND

Displays the expanded version of the variance components table, the table of coefficients, the random effect

predictions table, the table of fits and diagnostics, and the table of conditional means.

TSIMPLE

Displays the simple version of the variance components table, the table of coefficients, the random effect predictions

table, the table of fits and diagnostics, and the table of conditional means.

TMETHOD

Displays the method table.

TFACTOR

Displays the name, number of levels, and the values for all categorical factors in your model.

TITERATION

Displays the iteration history table that shows the value of −2 log likelihood for each iteration of the variance

components.

TVARIANCE

Displays the variance component estimates and the tests of whether the components are 0.

TCOVARIANCE

Display the asymptotic variance-covariance matrix of the variance component estimates.

TFIXEDEFFECT

Display tests of the fixed factors in the model.

TSUMMARY

Display S, R

, and R

(adj). The expanded version of the table adds AICc and BIC.

TCOEFFICIENTS

Displays the table of coefficients for fixed factors and covariates. If you do not specify FULL or NOFULL, Minitab

uses the preferences set in File > Options > Linear Models > Display of Results.

FULL

Displays the complete set of coefficients for categorical predictors.

NOFULL

Displays only the linearly independent coefficients.

TRANDOM

Displays the Best Unbiased Linear Predictions (BLUP) and tests of whether the BLUP are 0.

TEQUATION

Displays the marginal fitted equation. Minitab will display up to 50 equations. If you do not specify SINGLE or

SEPARATE, Minitab uses the preferences set in File > Options > Linear Models > Display of Results.

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SINGLE

If you want to view a single equation, rather than a separate equation for each factor level combination, use

SINGLE.

SEPARATE

To view the separate equations, use SEPARATE.

TCEQUATION

Displays the conditional fitted equation. Minitab will display up to 50 equations. If you do not specify SINGLE or

SEPARATE, Minitab uses the preferences set in File > Options > Linear Models > Display of Results.

SINGLE

If you want to view a single equation, rather than a separate equation for each factor level combination, use

SINGLE.

SEPARATE

To view the separate equations, use SEPARATE.

TDIAGNOSTICS K

Displays a table of diagnostics for the marginal fits and residuals. K = 0 displays diagnostics for only unusual

observations. K = 1 displays diagnostics for all observations.

TCONDITIONAL K

Displays a table of diagnostics for the conditional fits and residuals. K = 0 displays diagnostics for only unusual

observations. K = 1 displays diagnostics for all observations.

TMEANS

Displays the table of least squares means.

Storage

RESIDUALS C

Stores the residuals for the marginal fits.

SRESIDUALS C

Stores the standardized residuals for the marginal fits.

TRESIDUALS C

Stores the deleted t residuals for the marginal fits.

FITS C

Stores the marginal fitted values.

CRESIDUALS C

Stores the conditional residuals.

CSRESIDUALS C

Stores the standardized residuals for the conditional fits.

CTRESIDUALS C

Stores the deleted t residuals for the conditional fits.

CFITS C

Stores the conditional fitted values.

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HI C

Stores the leverages.

COOK C

Stores Cook's distance.

DFITS C

Stores DFITS.

COEFFICIENTS C

Stores the estimated coefficients.

BLUP C

Stores the best linear unbiased predictors.

XMATRIX M

Stores the design matrix for fixed effects terms.

ZMATRIX M

Stores the design matrix for random effects terms.

COVARIANCE M

Stores the variance-covariance matrix of the variance component estimates.

SMEANS C

Stores the least square means for the terms specified by MEANS.

VARCOMP C

Stores the estimates of the variance components in the same order as the terms appear after TERMS.

LOGLIKE C

Stores the −2 log likelihood values for all iterations.

FIXCOV M

Stores the variance-covariance matrix of the fixed parameters.

COMPARE: Session command for performing

multiple comparisons of means

COMPARE C

Generates comparisons based on a model from a general linear model or from a mixed effects model for response

If you have a general linear model or a mixed effects model stored in the worksheet, you can use COMPARE to

obtain multiple comparisons of means. Multiple comparisons of means allow you to examine which means are

different and to estimate by how much they are different.

You have the following choices when using multiple comparisons:

•

All pairwise comparisons or comparisons with a control

•

Which means to compare

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•

The method of comparison

•

How to display the results

Pairwise comparisons or comparison with a control

Use PAIRWISE when you do not have a control level but you would like to examine which pairs of means are different.

Use MCONTROL when you are comparing treatments to a control. When this method is suitable, it is inefficient to use

the all-pairwise approach, because the all-pairwise confidence intervals will be wider and the hypothesis tests less

powerful for a given family error rate.

Which means to compare

To specify which means to compare, enter terms from the model in the termslist for PAIRWISE or MCONTROL. If you

have 2 factors named A and B, entering A B will result in multiple comparisons within each factor. Entering A * B will

result in multiple comparisons for all level combinations of factors A and B.

The multiple comparisons method

You can choose from among four methods for both pairwise comparisons and comparisons with a control. Each

method provides simultaneous or joint confidence intervals, meaning that the confidence level applies to the set of

intervals computed by each method and not to each one individual interval. By protecting against false positives with

multiple comparisons, the intervals are wider than if there were no protection.

Some characteristics of the multiple comparison methods are summarized below. "Conservative" in this context indicates

that the true family error rate is less than the stated one.

PropertiesComparison method

Comparison to a control only, not proven to be conservativeDunnett

All pairwise differences only, not proven to be conservativeTukey

Most conservativeBonferroni

Conservative, but slightly less so than BonferroniSidak

Does not control the family error rateFisher's LSD

Display results

Minitab can present multiple comparison results in the following forms:

•

TGROUPING displays the grouping information table that highlights the significant and non-significant comparisons.

•

TMTEST displays the hypothesis test form which includes the adjusted p-values.

•

GINTPLOT displays the interval plots of the confidence the intervals for the mean difference between two groups.

Conditions that restrict comparisons for general linear models

For general linear models, comparisons cannot be done in the following situations:

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•

Comparisons are not available for terms that contain or interact with random factors. Nesting is a form of interaction.

For a model with random factors, you can do comparisons if you use a mixed effects model with the Restricted

Maximum Likelihood estimation method (REML). Suppose the model contains A B C A*B, where both A and C are

random, and B is fixed. Because A*B is a term in the model, no multiple comparison results for B are available even

though B is a fixed factor. However, if the model does not include A*B, multiple comparison results are available

for B.

There is a special case for a balanced design that has two factors. Suppose the model contains A B A*B, where A

is random, and B is fixed. Even though A*B is in the model, you can perform multiple comparisons for B.

•

Comparisons is disabled if you choose (1, 0) coding and the model is non-hierarchical. To enable Comparisons

for this case, choose (−1, 0, +1) coding or specify a hierarchical model.

Commands

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default value

of K is 95.

PAIRWISE [termlist}

Specifies pairwise comparisons using the terms in the termlist. The terms in the termlist must be in the model.

You must use at least one of the following sub-subcommands with PAIRWISE:

TUKEY

BONFERRONI

SIDAK

FISHER

MCONTROL [termlist]

Specifies comparisons with a control using the terms in the termlist. The terms in the termlist must be in the

model.

You must use at least one of the following sub-subcommands with PAIRWISE:

DUNNETT

BONFERRONI

SIDAK

FISHER

LEVELS C K...C K

You must use LEVELS with MCONTROL. Use LEVELS to provide a factor (C) with a control level (K). You must

specify a control level for each factor that you list in MCONTROL. If these levels are text or date/time, enclose

each with double quotation marks.

ALTERNATIVE K

ALTERNATIVE specifies the alternative hypothesis for comparisons to the control level.

Alternative hypothesisValue of K

Determines whether the treatment means are less than the control mean−1

Determines whether the treatment means are equal to the control mean0

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Alternative hypothesisValue of K

Determines whether the treatment means are greater than the control mean1

GINTPLOT

Displays interval plots that represent the confidence interval for the mean difference between two groups. A

separate graph is created for each comparison.

TGROUPING

Displays the grouping information table for each comparison. This table highlights the significant and non-significant

comparisons for each selected comparison method.

TMTEST

Displays the multiple comparison test table. This table displays the hypothesis test form of the comparison output,

which includes the differences of the means, the numeric values for the confidences intervals, and the adjusted

p-values.

MANOVA: Session command for performing a

general MANOVA

MANOVA C...C = C...C

Performs a general MANOVA. The columns specified before the equal sign are the responses, and the columns

after the equal sign are the factors.

Use general MANOVA to perform multivariate analysis of variance (MANOVA) with balanced and unbalanced

designs, or if you have covariates. This procedure takes advantage of the data covariance structure to simultaneously

test the equality of means from different responses.

Calculations are done using a regression approach. A full rank design matrix is formed from the factors and

covariates and each response variable is regressed on the columns of the design matrix.

Factors can be crossed or nested, but they cannot be declared as random. You can work around this restriction

by specifying the error term to test model terms. Covariates can be crossed with each other or with factors, or

nested within factors. You can analyze up to 50 response variables with up to 31 factors and 50 covariates at one

time.

With the MANOVA subcommand, you can specify model terms for a custom multivariate test and designate the

error term in Error. Minitab performs four multivariate tests (see the MANOVA subcommand) for those terms.

This option is most useful when you have factors that you consider as random factors. Model terms that are

random or that are interactions with random terms may need a different error term than general MANOVA

supplies. You can determine the appropriate error term by entering one response variable with General Linear

Model, choose to display the expected mean square, and determine which error term was used for each model

terms.

If you specify an error term, it must be a single term that is in the model. This error term is used for all requested

tests. If you have different error terms for certain model terms, enter each separately and exercise the general

MANOVA dialog for each one. If you do not specify an error term, Minitab uses MSE.

For more information, go to How to specify the model for GLM on page 1167 and How to enter data for ANOVA

and GLM on page 1158.

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Model

MANOVA [termlist [ / errorterm]]

Performs four multivariate tests—Wilks' test, Lawley-Hotelling test, Pillai's test, and Roy's largest root test—for

each term in the model. If you include a termlist, MANOVA does the four multivariate tests for each term listed.

If you specify an errorterm, it must be a single term that is in the model. MANOVA then uses this errorterm in all

tests. If you do not specify an errorterm, Minitab uses the error associated with MSE, as in the univariate case.

All four tests are based on two SSCP (sums of squares and cross products) matrices: H = the hypothesis matrix

and E = the error matrix.

If an error term is not specified on MANOVA, then the adjusted SSCP matrix is used for H and the SSCP matrix

associated with MSE is used for E. If an error term is specified on MANOVA, the sequential SSCP matrices associated

with H and E are used. Using sequential SSCP matrices guarantees that H and E are statistically independent.

COVARIATES C...C

The columns listed are used as covariates. If COVARIATES is used, it must be the first subcommand. This restriction

is needed to allow proper error checking. You may have up to 50 covariates.

Options

WEIGHTS C

Performs a weighted least squares fit. The weights must be greater than or equal to zero. An n x n matrix W is

formed with the column of weights as its diagonal and zeros elsewhere. The regression coefficients are calculated

by (X' WX)

−1

(X' WY).

This is equivalent to minimizing the weighted SS Error:

PREDICT

Computes the fitted Y's, or for given values of the predictors. PREDICT displays a table that contains the fitted

Y's, standard errors of the fitted Y's, a 95% confidence interval, and a 95% prediction interval. E...E may be a list

of values, one for each predictor, a list of columns, one for each predictor, or a mix of values and columns.

The prediction interval computed by PREDICT assumes a weight of 1. If you used the WEIGHT subcommand with

values other than 1, you should adjust the prediction interval values manually.

CONFIDENCE K

Specifies a confidence level. For example, for a 90% confidence level, enter CONFIDENCE 90. The default

value of K is 95.

PFITS C

Stores the fits.

PSDFITS C

Stores the estimated standard errors of the fits.

CLIMITS C C, ..., C C

Stores the lower and upper confidence limits.

PLIMITS C C, ..., C C

Stores the lower and upper prediction limits.

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TEST termlist / errorterm

Use TEST to specify your own tests. Termlist is a list of terms in the model. List them exactly as on the MANOVA

subcommand. Error term is the term to be used as the denominator for the F-test. This can be a term in the model,

a linear combination of terms in the model, or MSE (denoted by the word ERROR). Only available via command

line.

TOLERANCE K [K]

Use TOLERANCE to force Minitab to keep a predictor in the model which is either highly correlated with another

predictor or which is nearly constant. Lowering tolerance by giving very small argument values can prevent Minitab

from eliminating problematic predictor columns from the model.

DescriptionK

1 – R-squared, where R-squared is the value resulting from regressing one predictor on the

remaining predictors. The default is 1E – 18. If (1 – R-squared) is < (1E – 18), the predictor is

removed from the equation.

First K

The parameter for forcing in a variable which is nearly constant. The default is 2E – 21. If the

coefficient of variation of a predictor (the standard deviation / the mean) is < square root (2E

– 21), then the predictor is considered essentially constant and is removed from the equation.

Second K

Both Ks must be positive numbers.

Graphs

RTYPE K

Specifies the type of residual to plot with the graph subcommands.

Type of residualsValue of K

Regular or raw residuals (RESIDUALS)1 (default)

Standardized residuals (SRESIDUALS)2

Deleted Studentized residuals (TRESIDUALS)3

GHISTOGRAM

Displays a histogram of the residuals.

GNORMAL

Displays a normal probability plot of the residuals.

GFITS

Plots the residuals versus the fitted values.

GORDER [C]

Plots the residuals versus the order of the data. The row number for each data point is shown on the x-axis (for

example, 1 2 3 4...n). Optionally, specify a column C that defines the order.

GFOURPACK

Displays a layout of a histogram of the residuals, a normal probability plot of the residuals, residuals vs fitted

values, and residuals vs order of the data.

GVARIABLE C...C

Displays a separate graph for the residuals versus each specified column.

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Results

MEANS termlist

Displays a table of adjusted means (sometimes called least squares means) corresponding to each term listed.

Terms listed on MEANS must also be listed on MANOVA. If more than one MEANS subcommand is specified,

only the last one is used.

EMS

Displays a table that contains expected mean squares, estimated variance components, and the error term (the

denominator) used in each exact F-test. If there is no exact F-test for a term, the expected mean squares allow

you to determine how to construct an approximate F-test using the subcommand TEST.

The estimates of the variance components are the usual unbiased analysis of variance estimates. They are obtained

by setting each calculated MS equal to its EMS. This gives a system of linear equations in the unknown variance

components. This system is then solved. Unfortunately, this method can result in negative estimates, which should

be set to zero. Minitab, however, prints the negative estimates because they sometimes indicate that the model

being fit is inappropriate for the data.

Terms that are fixed do not have variance components estimated.

BRIEF K

Controls the amount of output with a value in K. You can also use BRIEF as a main command. When you use BRIEF

as a main command, it affects all other commands that use BRIEF to control the amount of output.

Output that is displayedValue of K

Minitab displays no output, but performs all specified storage and displays the following

error messages, warnings, prompts, and notes; graphs; WRITE to the screen.

Minitab displays the ANOVA table.1

K = 1, and Minitab displays the table of factor levels, coefficients for terms involving

covariates, and unusual observations.

2 (default)

K = 2 output, and Minitab displays all the coefficients.3

SSCP

Displays the hypothesis matrix, H, corresponding to each term specified by MANOVA, and the error matrix, E.

EIGEN

Displays a table containing the eigenvalues and eigenvectors for the (nonsymmetric) matrix, E**-1 H. These are

the eigenvalues that are used to calculate the four MANOVA tests. A separate table is printed for each term

specified on MANOVA.

Note If an eigenvalue is repeated, then the corresponding eigenvectors are not unique. In this case, the eigenvectors Minitab displays

and those in books or other software might not agree. However, the MANOVA tests are always unique.

PARTIAL

Displays a matrix of partial correlations. These are the correlations among the residuals or, equivalently, the

correlations among the responses conditioned on the model. The formula for the matrix is W**-.5 E W**-.5, where

E is the error matrix and W has the diagonal of E as its diagonal and 0's elsewhere.

NOUNIVARIATE

Suppresses the univariate output. Only the multivariate output is displayed.

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INTERACT terms

Used with SMEANS to store means for two-way interactions. List the model terms for which you calculate means

for level pairs. The means will be calculated by the order of terms and levels.

SMEANS C...C

Stores means for two-way interactions, main effects, and the overall mean. Use with INTERACT to store means

for two-way interactions. Specify a storage column for each response variable.

Storage

FITS C...C

Stores fitted values, using one column for each response.

RESIDUALS C...C

Stores residuals, using one column for each response variable. Residual = (response – fit).

SRESIDUALS C...C

Stores the standardized residuals, using one column for each response variable.

TRESIDUALS C...C

Stores the deleted Studentized residuals, using one column for each response variable.

HI C

Stores leverages.

COOKD C...C

Stores Cook's distance.

DFITS C...C

Stores the DFITS (also called DFFITS), using one column for each response.

XMATRIX M

Stores the design matrix corresponding to your model in M.

COEFFICIENTS C...C

Stores the coefficients for a model, using one column for each response. These are the same coefficients that are

printed under BRIEF 3. They correspond to the design matrix stored by XMATRIX. Thus, if M1 contains the design

matrix and C1 the coefficients, then M1 times C1 gives the fitted values.

NESTED: Session command for performing a

fully-nested ANOVA

NESTED C...C = C...C

Analyzes fully nested (hierarchical) designs. All factors are assumed to be random. Designs can be unbalanced.

In this case, F and p-values are not displayed. You can calculate these yourself using the expected mean squares

that are displayed.

You can analyze up to 50 response variables and up to 9 factors on one NESTED command. The columns specified

before the equal sign are the responses, and the columns after the equal sign are the factors. You can also have

replicates. One row then corresponds to one observation, giving the value of each response and the level of each

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factor for that observation. Factor levels can be any real numbers. They do not need to be consecutive or in any

special order.

VARTEST: Session command for performing an equal

variances test

VARTEST C...C

If you do not specify UNSTACKED, VARTEST performs tests for equal variances with all response data in one C

and factor level information in additional columns.

If you do specify UNSTACKED, VARTEST performs tests for equal variances with data from each factor level in a

different column C...C.

UNSTACKED

Indicates that data for each factor level are in different columns.

Options

CONFIDENCE K

Specifies the confidence level for the Bonferroni simultaneous confidence intervals and also the significance level

(denoted by α or alpha) for the multiple comparison intervals, and the tests. The default is 95, which corresponds

to a confidence level of 95% and an α = 1 – (95 / 100) = 0.05.

USEBARTLETT

Specifies to use the test based on the normal distribution instead of the multiple comparisons method and Levene's

method. If you have only 2 factor levels, then Minitab performs the F-test. If you have 3 or more factor levels,

then Minitab performs Bartlett's test.

The F-test and Bartlett's test are accurate only for normally distributed data. Any departure from normality can

cause these tests to yield inaccurate results. However, if the data conform to the normal distribution, then the

F-test and Bartlett's test are typically more powerful than either the multiple comparisons method or Levene's

method.

Graphs

GINTERVAL

Displays a graphical summary that includes the multiple comparison intervals for standard deviations and test

results by default. The multiple comparison intervals are not confidence intervals of the individual standard

deviations. The multiple comparison intervals are only useful for comparing the population standard deviations.

If USEBARTLETT is used, then GINTERVAL displays Bonferroni confidence intervals of the standard deviations.

GINDPLOT

Displays individual value plot.

GBOXPLOT

Displays a boxplot.

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Results

NODEFAULT

Minitab displays all output tables by default. You do not have to enter the subcommands. If you enter NODEFAULT,

then each table is only displayed if you enter the specific subcommand.

TMETHOD

Displays the method table which includes the null hypothesis, the alternative hypothesis, and the significance

level (denoted by α or alpha).

TBONFERRONI

Displays the table of Bonferroni simultaneous confidence intervals for the standard deviations of each factor level.

TTEST

Displays the test table which includes the p-values for the hypothesis tests.

Storage

STDEVS C

Stores the standard deviation of each factor level in column C.

VARIANCES C

Stores the variance of each factor level in column C.

SBONFERRONI C C

Stores Bonferroni confidence limits for the standard deviation of each factor level. The lower limits are stored in

the first column C and the upper limits is stored in the second column C.

SMCI C C

Stores the limits of the multiple comparisons intervals for each factor level. The lower limits are stored in the first

column C and the upper limits is stored in the second column C.

SFPVALUE C

Stores the p-value for Bartlett's test (or the F-test if there are only 2 factor levels).

SLPVALUE C

Stores the p-value for Levene's test.

SMPVALUE C

Stores the p-value for the multiple comparison test.

INTPLOT: Session command for creating an interval

plot

INTPLOT C...C

INTPLOT (C...C) * C

Use to plot means and confidence intervals for one or more variables. An interval plot illustrates both a measure

of central tendency and the variability of the data.

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Minitab Statistical Software ANOVA

By default, Minitab displays confidence intervals, but you can change the display type to standard error bars using

the INTBAR subcommand.

The data must be numeric or date/time. The categorical grouping data can be numeric, date/time, or text.

INTPLOT C...C displays a separate interval plot for each graph variable.

INTPLOT (C...C) * C displays a separate graph for each C on the left, with an interval for each category of the C on

the right.

The following table shows a few of the possible ways to generate interval plots.

What the command language displaysCommand language

Two graphs: interval plots of Sales and of Advertis

INTPLOT 'Sales' 'Advertis'.

A graph with intervals representing yearly sales

INTPLOT 'Sales'*'Year'.

Scale