Model Fitting: Logistic Regression

Overview of the Logistic Regression Analysis

The Logistic Regression analysis fits a logistic regression model by using the method of maximum likelihood estimation.

If $X_ i$ are explanatory variables and p is the response probability to be modeled, the logistic model has the form

\[  \log (p/(1-p)) = b_0 + b_1 X_1 + b_2 X_2 + \ldots + b_ m X_ m  \]

where the $b_ i$ are regression coefficients.

The explanatory variables in the Logistic Regression analysis can be interval variables or nominal variables (also known as classification variables). You can also specify more complex model terms such as interactions and nested terms. Any term specified in the model is referred to as an effect, whether it is the main effect of a variable, or a classification variable, or an interaction, or a nested term.

You can run a Logistic Regression analysis by selecting AnalysisModel FittingLogistic Regression from the main menu. The computation of the estimated regression coefficients, confidence limits, and related statistics is implemented by calling the LOGISTIC procedure in SAS/STAT software. See the documentation for the LOGISTIC procedure in the SAS/STAT User's Guide for additional details.