When including predictor variables in logistic regression, they may need to be altered or transformed prior to inclusion in a model. One type of transformation might be to normalize predictors so that each variable is on the same scale. This ensures that no predictor variable is overweighted in analysis simply because its natural range has larger numbers. When discussing logistic regression, we also addressed two other types of transformations to predictor variables: (1) binning and (2) x^2 transformations. For both binning and x^2 transformations describe one specific instance or one reason why each transformation may be necessary.