The set of variables that make up each class is assumed to be multivariate normal with a common covariance matrix. When none of the unselected variables meets the entry criterion, the forward selection process stops. Backward elimination begins with all variables in the model except those that are linearly dependent on previous variables in the VAR statement. At each step, the variable that contributes least to the discriminatory power of the model as measured by Wilks' Lambda is removed. When all remaining variables meet the criterion to stay in the model, the backward elimination process stops.
Stepwise selection begins, like forward selection, with no variables in the model.
At each step, the model is examined. If the variable in the model that contributes least to the discriminatory power of the model as measured by Wilks' lambda fails to meet the criterion to stay, then that variable is removed. Otherwise, the variable not in the model that contributes most to the discriminatory power of the model is entered. When all variables in the model meet the criterion to stay and none of the other variables meet the criterion to enter, the stepwise selection process stops. Stepwise selection is the default method of variable selection.