Stepwise Logistic Regression
Stepwise Logistic Regression methods, specifically the Forward Stepwise and Backward Stepwise methods, are used to perform a stepwise selection of predictor variables.
During the forward step of stepwise model building, if two or more effects have p-values that are so small as to be virtually indistinguishable from 0, Statistica® selects the effect with the largest score statistic if the degrees of freedom for all effects in question are equal. If the effects differ with respect to the degrees of freedom, the Score statistics are normalized using the Wilson-Hilferty transformation, and the effect with the largest transformed value is entered into the model. For the backward step, if the p-values for two or more effects are virtually indistinguishable from 1, Statistica® removes the effect with the smallest Wald statistic in the case of equal degrees of freedom and the smallest normalized value in the case of unequal degrees of freedom.