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. ...