GLZ Syntax - Example 2: Stepwise Multiple Regression

This example illustrates the specification for a stepwise multiple regression problem with categorical predictors. When categorical predictor variables or effects have more than a single degree of freedom, the stepwise and best-subset procedures in STATISTICA GLZ ensure that the coded (only sigma-restricted parameterization is allowed in stepwise and best subsets regression) variables representing the categorical predictors are moved in or out of the model as a block (so that always complete multi-degree of freedom effects are included or excluded from the final model).

Note that brief descriptive comments are enclosed in curly brackets. You can run this example with the example data file Exp.sta, Normal distribution and Identity link in the Startup Panel.

GLZ;

{ Dependent or response variable: }

   RESPONSE = STRESS_R;

{ Specification of grouping variables (factors); note that

  optional grouping codes (values) are specified to identify

  the (selected) groups in the categorical predictors. }

   GROUPS = GENDER ("MALE" "FEMALE")

            TIME   (1 2 3);

{ Specification of continuous predictor variables (covariates) }

   COVARIATE = CORRECT1 CORRECT2 CORRECT3;

{ Here the bar operator is used to construct the full factorial

  design for the categorical predictor variables; the bar operator

  will evaluate to all main effects and interactions

  between the grouping variables. }

   DESIGN   =  CORRECT1+CORRECT2+CORRECT3+

               GENDER | TIME;

{ Forward stepwise regression is requested as the model building

  method. }

   MBUILD   = FSTEPWISE;

{ The maximum number of steps is 10. }

   MAXSTEP  = 10;

{ p to enter is 0.10. }

   P1ENTER  = .10;

{ p to remove is 0.10. }

   P2REMOVE = .10;

For more examples, see GLZ Syntax Examples.