Logistic regression has a long tradition with widely varying applications such as modeling dose-response data and purchase-choice data. Unfortunately, many introductory statistics courses do not cover this fairly simple method. Many texts in categorical statistics cover it (Agresti 1998), in addition to texts on logistic regression (Hosmer and Lemeshow 1989). Some analysts use the method with a different distribution function, the normal. In that case, it is called probit analysis. Some analysts use discriminant analysis instead of logistic regression because they prefer to think of the continuous variables as Ys and the categories as Xs and work backwards. However, discriminant analysis assumes that the continuous data are normally distributed random responses, rather than fixed regressors.

Simple logistic regression is a more graphical and simplified version of the general facility for categorical responses in the Fit Model platform. For examples of more complex logistic regression models, see Logistic Regression Models in the Fitting Linear Models book.