Fitting Linear Models • Stepwise Regression Models
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Stepwise Regression Models
Find a Model Using Variable Selection
Stepwise regression is an approach to selecting a subset of effects for a regression model. It can be useful in the following situations:
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There is little theory to guide the selection of terms for a model.
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You want to interactively explore which predictors seem to provide a good fit.
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You want to improve a model’s prediction performance by reducing the variance caused by estimating unnecessary terms.
For categorical predictors, you can do the following:
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Choose from among various rules to determine how associated terms enter the model.
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Enforce effect heredity.
The Stepwise platform also enables you to explore all possible models and to conduct model averaging.
Stepwise Regression: An Overview (six-minute video)
Learn how stepwise regression can help you efficiently arrive at a successful model.
Contents 
Overview of Stepwise Regression
Example Using Stepwise Regression
The Stepwise Report
Stepwise Platform Options
Stepwise Regression Control Panel
Current Estimates Report
Step History Report
Models with Crossed, Interaction, or Polynomial Terms
Models with Nominal and Ordinal Effects
Construction of Hierarchical Terms
Example of a Model with a Nominal Term
Example of the Restrict Rule for Hierarchical Terms
Performing Binary and Ordinal Logistic Stepwise Regression
The All Possible Models Option
The Model Averaging Option
Using Validation
Validation Set with Two or Three Values
K-Fold Cross Validation