Logistic Regression Using SAS: Theory and Application, Second Edition
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 nonlinear 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). This book is part of the SAS Press program.
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Chapter 4 Logit Analysis of Contingency Tables
Chapter 5 Multinomial Logit Analysis
Chapter 6 Logistic Regression for Ordered Categories
Chapter 7 Discrete Choice Analysis
Allison Analysis of Maximum binary logit blackd Chi-Square DF Chi-Square Pr>ChiSq Intercept ChiSq coefficients conditional logit model confidence intervals contingency table Copyright correlation covariates culp cumulative logit model data set degrees of freedom dependent variable Deviance and Pearson DF Estimate Standard dichotomous effect equation Error Wald Chi-Square Estimate Standard Error Estimates Parameter DF example explanatory variables frequencies GLIMMIX Here’s interaction Likelihood Estimates Parameter Likelihood Parameter Estimates likelihood ratio Log Likelihood Logistic Regression logit model loglinear model Maximum Likelihood Estimates method mixed model MODEL statement multinomial logit multinomial logit model null hypothesis odds ratio Odds Ratio Estimates option overdispersion p-value Parameter DF Estimate Paul Pearson chi-square Poisson regression predicted probability PROC GENMOD PTSD regression model Regression Using SAS sample SAS Institute Inc saturated model Second Edition shown in Output smaller is better Standard Error Wald Theory and Application Value/DF variance Wald 95 Wald Chi-Square Pr>ChiSq wallet whitvic