Publication date: 07/30/2020

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.

Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).