Applied Logistic Regression
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More About This Title Applied Logistic Regression


Shows how to model a binary outcome variable from a linear regression analysis point of view. Develops the logistic regression model and describes its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariates. Following establishment of the model there is discussion of its interpretation. Several data sets are the source of the examples and the exercises, and a number of software packages are used to analyze data sets, including BMDP, EGRET, GLIM, SAS, and SYSTAT.


Introduction to the Logistic Regression Model.

The Multiple Logistic Regression Model.

Interpretation of the Coefficients of the Logistic Regression Model.

Model-Building Strategies and Methods for Logistic Regression.

Assessing of the Fit of the Model.

Application of Logistic Regression with Different Sampling Models.

Logistic Regression for Matched Case-Control Studies.

Special Topics.