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.