Stepwise Regression Models
Fit a Model Using Variable Selection
Some features in this platform are available only in JMP Pro and noted with this icon.
The Stepwise personality of the Fit Model platform enables you to use stepwise methods to fit regression models, explore all possible models for a set of regressors, and conduct model averaging.
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 that is caused by estimating unnecessary terms.
For model selection, you can perform the following tasks:
• choose from among various rules to determine how associated terms enter the model
• enforce effect heredity
• 
use cross validation criteria with respect to a holdout set
• fit and rank all possible models for a set of regressors
• conduct model averaging
Figure 5.1 Stepwise Report Window
