ABSTRACT
Logistic regression and discriminant analysis, like multiple regression, are useful when you want to predict an outcome or dependent variable from a set of predictor variables. They are similar to a linear regression in many ways. However, logistic regression and discriminant analysis are more appropriate when the dependent variable is categorical. Logistic regression is useful because it does not rely on some of the assumptions on which multiple regression and discriminant analysis are based. As with other forms of regression, multicollinearity (high correlations among the predictors) can lead to problems for both logistic and discriminant analysis.
