Logistic Regression Using SAS: Theory and Application, Second Edition
- 5h 19m
- Paul D. Allison
- SAS Institute
- 2012
If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's Logistic Regression Using SAS: Theory and Application, Second Edition, is for you! Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logistic regression, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. The second edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing non-linear effects). Also new is coverage of PROC SURVEYLOGISTIC (for complex samples), PROC GLIMMIX (for generalized linear mixed models), PROC QLIM (for selection models and heterogeneous logit models), and PROC MDC (for advanced discrete choice models).
In this Book
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Introduction
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Binary Logistic Regression with PROC LOGISTIC: Basics
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Binary Logistic Regression: Details and Options
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Logit Analysis of Contingency Tables
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Multinomial Logit Analysis
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Logistic Regression for Ordered Categories
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Discrete Choice Analysis
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Logit Analysis of Longitudinal and Other Clustered Data
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Regression for Count Data
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Loglinear Analysis of Contingency Tables
