Modeling and Multivariate Methods - SAS

Chapter 4
Fitting Stepwise Regression Models
Using the Fit Model Platform
Stepwise regression is an approach to selecting a subset of effects for a regression model. Use stepwise
regression when there is little theory to guide the selection of terms for a model, and the modeler wants to
use whatever seems to provide a good fit. The approach is somewhat controversial. The significance levels
on the statistics for selected models violate the standard statistical assumptions because the model has been
selected rather than tested within a fixed model. On the positive side, the approach has been helpful for 30
years in reducing the number of terms. The book Subset Selection in Regression, by A. J. Miller (1990), brings
statistical sense to model selection statistics.
This chapter uses the term significance probability in a mechanical way to represent that the calculation
would be valid in a fixed model, recognizing that the true significance probability could be nowhere near the
reported one.
The Stepwise Fit also includes features for looking at all possible models (including heredity factors) and
model averaging.