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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.
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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Stepwise Discriminant Analysis |