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Logistic Regression Using the SAS System Theory and Application

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ISBN-10: 0471221759

ISBN-13: 9780471221753

Edition: 2002

Authors: Paul D. Allison, SAS Institute Staff

List price: $100.95
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Description:

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.
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Book details

List price: $100.95
Copyright year: 2002
Publisher: John Wiley & Sons, Incorporated
Publication date: 12/21/2001
Binding: Paperback
Pages: 308
Size: 8.25" wide x 10.50" long x 0.50" tall
Weight: 1.540

Paul D. Allison, Ph.D., is Professor of Sociology at the University of Pennsylvania where he teaches graduate courses in methods and statistics. He is also the founder and president of Statistical Horizons LLC which offers short courses on a wide variety of statistical topics. After completing his doctorate in sociology at the University of Wisconsin, he did postdoctoral study in statistics at the University of Chicago and the University of Pennsylvania. He has published eight books and more than 60 articles on topics that include linear regression, log-linear analysis, logistic regression, structural equation models, inequality measures, missing data, and survival analysis. Much of his early research focused on career patterns of academic scientists. At present, his principal research is on methods for analyzing longitudinal data, especially those for determining the causes and consequences of events, and on methods for handling missing data. A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology. In 2010 he was named a Fellow of the American Statistical Association. He is also a two-time winner of the American Statistical Association's award for "Excellence in Continuing Education."nbsp;

Acknowledgments
Introduction
Binary Logit Analysis: Basics
Binary Logit Analysis: Details and Options
Logit Analysis of Contingency Tables
Multinomial Logit Analysis
Logit Analysis for Ordered Categories
Discrete Choice Analysis
Logit Analysis of Longitudinal and Other Clustered Data
Poisson Regression
Loglinear Analysis of Contigency Tables
Appendix
References
Index