Setting the Selection Options

Specify a selection method to select the variables to include in the model. The available options depend on the selection method.

Selection Method	Description
None	No selection method is used, and all variables are included in the model.
Stepwise selection	Stepwise selection begins with no variables in the model. To add a variable to the model, the F statistic must be significant. The significance level is specified in the Significance level for adding a variable text box. At each step, all variables in the model are evaluated for retention. Any variable that does not have a significant F statistic is removed. The significance level is specified in the Significance level for retaining a variable text box. The stepwise process ends when either of these conditions is met: no variable outside the model has an F statistic that is significant at the significance level for adding a variable, and every variable in the model is significant at the significance level to stay in the model the variable that meets the criterion for addition to the model is the variable that was deleted from the model in the previous step
Forward selection	The forward selection method begins with no variables in the model. For each of the explanatory variables, this method calculates F statistics that reflect the variable's contribution to the model. The p-values for these F statistics are compared to the significance level in the Significance level for adding a variable text box. By default, this value is 0.15. If no F statistic has a significance level greater than this value, the forward selection stops. Otherwise, the forward selection method adds the variable with the largest F statistic to the model. The forward selection method then calculates F statistics again for the variables that remain outside the model, and the evaluation process is repeated. Thus, variables are added one by one to the model until no remaining variable produces a significant F statistic. After a variable is added to the model, it stays there.
Backward selection	The backward selection method begins by calculating F statistics for all the explanatory variables. Then the variables are deleted from the model one by one until all the variables that remain in the model produce significant F statistics. The significance level is specified in the Significance level for retaining a variable text box. By default, this value is 0.15. At each step, the variable that shows the smallest contribution to the model is deleted.