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Fitting Linear Models > Stepwise Regression Models
Publication date: 11/10/2021

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:

There is little theory to guide the selection of terms for a model.

You want to interactively explore which predictors seem to provide a good fit.

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:

Choose from among various rules to determine how associated terms enter the model.

Enforce effect heredity.

The Stepwise platform also enables you to explore all possible models and to conduct model averaging.


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

Example of the Combine Rule

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

Example Using Logistic Stepwise Regression

The All Possible Models Option

Example Using the All Possible Models Option

The Model Averaging Option

Example Using the Model Averaging Option

Validation Options in Stepwise Regression

Validation Set with Two or Three Values in Stepwise Regression
K-Fold Cross Validation in Stepwise Regression
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