This section describes how crossed, interaction, and polynomial terms are handled in the Stepwise personality of the Fit Model platform. Some stepwise regression models, especially those associated with experimental designs, involve interaction terms. For continuous factors, these are products of the columns that represent the effects. For nominal and ordinal factors, interactions are defined by model terms that involve products of terms that represent the categorical levels.
When there are interaction terms, you can impose a restriction on the model selection process so that lower-order components of higher-order effects are included in the model. This is suggested by the principle of Effect Heredity. See “Effect Heredity” in the Design of Experiments Guide. For example, if a two-way interaction is included in a model, its component main effects (precedents) should be included as well.