Model Fitting: Logistic Regression: Model Fitting: Logistic Regression

Model Fitting: Logistic Regression

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

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

$\log(p/(1-p)) = b_0 + b_1 x_1 + b_2 x_2 + ... + b_m x_m$

where the

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 Analysis $\blacktriangleright\,$ Model Fitting $\blacktriangleright\,$ Logistic 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. See the documentation for the LOGISTIC procedure in the SAS/STAT User's Guide for additional details.

Model Fitting: Logistic Regression

Example

Specifying the Logistic Regression Analysis

Analysis of Selected Variables