Specifying the Model Selection Options

Option	Description
Model Selection
Selection method	specifies the model selection method for the model. The task performs model selection by examining whether effects should be added to or removed from the model according to the rules that are defined by the selection method. Here are the valid values for the selection methods: None fits the full model. Forward selection starts with no effects in the model and adds effects based on the Significance level to add an effect to the model option. Backward elimination starts with all the effects in the model and deletes effects based on the value in the Significance level to remove an effect from the model option.
Selection method (continued)	Fast backward elimination uses a computational algorithm of Lawless and Singhal (1978). This algorithm computes a first-order approximation to the remaining slope estimates for each subsequent elimination of a variable from the model. Variables are removed from the model based on these approximate estimates. This selection method is extremely efficient because the model is not refitted for every variable removed. Stepwise selection is similar to the forward selection model. However, effects that are already in the model do not necessarily stay there. Effects are added to the model based on the Significance level to add an effect to the model option and are removed from the model based on the Significance level to remove an effect from the model option. Stepwise selection with fast backward elimination uses a computational algorithm of Lawless and Singhal. This algorithm computes a first-order approximation to the remaining slope estimates for each subsequent elimination of a variable from the model. Variables are removed from the model based on these approximate estimates. This selection method is extremely efficient because the model is not refitted for every variable removed.
Details
Display selection process details	specifies how much information about the selection process to include in the results. You can choose to display a summary, details for each step of the selection process, or all of the information about the selection process.
Maintain hierarchy of effects	specifies how the model hierarchy requirement is applied and that only a single effect or multiple effects can enter or leave the model at one time. For example, suppose you specify the main effects A and B and the interaction AB in the model. In the first step of the selection process, either A or B can enter the model. In the second step, the other main effect can enter the model. The interaction effect can enter the model only when both main effects have already been entered. Also, before A or B can be removed from the model, the AB interaction must first be removed. Model hierarchy refers to the requirement that, for any term to be in the model, all effects contained in the term must be present in the model. For example, in order for the interaction AB to enter the model, the main effects A and B must be in the model. Likewise, neither effect A nor B can leave the model while the interaction AB is in the model.