ABSTRACT
Similar to discriminant analysis, logistic regression allows one to predict a discrete outcome, such as group membership, from a set of variables that may be continuous, discrete, dichotomous, or a mix of any of these. Generally, the dependent variable is dichotomous, such as male/female, smoker/nonsmoker or success/failure. While discriminant analysis is also used to predict group membership with only two groups, logistic regression is more flexible in that it has no assumptions about the distributions of the predictor variables-the predictor variables do not have to be normally distributed, linearly related, or of equal variance/covariance across the groups.
