Setting 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 value of the specified criterion. Backward elimination starts with all the effects in the model and deletes effects based on the value of the specified criterion. 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 values of the specified criteria.
Add/remove effects with	specifies the criterion to use to add or remove effects from the model.
Stop adding/removing effects with	specifies the criterion to use to stop adding or removing effects from the model.
Select best model by	specifies the criterion to use to identify the best fitting model.
Selection Statistics
Model fit statistics	specifies which model fit statistics are displayed in the fit summary table and the fit statistics tables. If you select Default fit statistics, the default set of statistics that are displayed in these tables includes all the criteria used in model selection. Here are the additional fit statistics that you can include in the results: Adjusted R-square Akaike’s information criterion Akaike’s information criterion corrected for small-sample bias Bayesian information criterion Mallows’ Cp Press statistic, which specifies the predicted residual sum of squares statistic R-square Schwarz’s Bayesian information criterion
Selection Plots
Criteria plots	displays plots for these criteria: adjusted R-square, Akaike’s information criterion, Akaike’s information criterion corrected for small-sample bias, and the criterion used to select the best fitting model.
Coefficient plots	displays these plots: a plot that shows the progression of the parameter values as the selection process proceeds a plot that shows the progression of the criterion used to select the best fitting model
Details
Selection process details	specifies how much information about the selection process to include in the results. You can display a summary, details for each step of the selection process, or all of the information about the selection process.