Logistic Regression Using the SAS System

Written in an informal and non-technical style, this book first explains the theory behind logistic regression and then shows how to implement it using the SAS System. Allison includes several detailed, real-world examples of the social sciences to provide readers with a better understanding of the material. He also explores the differences and similarities among the many generalizations of the logistic regression model. Acknowledgments.

Chapter 1. Introduction.

Chapter 2. Binary Logit Analysis: Basics.

Chapter 3. Binary Logit Analysis: Details and Options.

Chapter 4. Logit Analysis of Contingency Tables.

Chapter 5. Multinomial Logit Analysis.

Chapter 6. Logit Analysis for Ordered Categories.

Chapter 7. Discrete Choice Analysis.

Chapter 8. Logit Analysis of Longitudinal and Other Clustered Data.

Chapter 9. Poisson Regression.

Chapter 10. Loglinear Analysis of Contigency Tables.

Appendix.

References.

Index.

Paul D. Allison is a Professor of Sociology and Epidemiology at the University of Pennsylvania where he teaches graduate courses in survival analysis and categorical data analysis. Every summer he teaches a five-day workshop about logistic regression that is attended by researchers from around the United States and Canada. Besides his numerous statistical papers, he has also published extensively on the subject of scientists' careers.