Overview of the Logistic Regression Analysis

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Overview of the Logistic Regression Analysis

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

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 → → 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.

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