Stepwise Discriminant analysis

Figure 1. Variables not in the analysis
Variables not in the analysis

When you have a lot of predictors, the stepwise method can be useful by automatically selecting the "best" variables to use in the model. The stepwise method starts with a model that doesn't include any of the predictors. At each step, the predictor with the largest F to Enter value that exceeds the entry criteria (by default, 3.84) is added to the model.

The variables left out of the analysis at the last step all have F to Enter values smaller than 3.84, so no more are added.

Figure 2. Variables in the analysis
Variables in the analysis

This table displays statistics for the variables that are in the analysis at each step. Tolerance is the proportion of a variable's variance not accounted for by other independent variables in the equation. A variable with very low tolerance contributes little information to a model and can cause computational problems.

F to Remove values are useful for describing what happens if a variable is removed from the current model (given that the other variables remain). F to Remove for the entering variable is the same as F to Enter at the previous step (shown in the Variables Not in the Analysis table).

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