Logistic Regression Using SAS: Theory and Application, Second EditionIf 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. |
Contents
Details and Options | 39 |
Chapter 4 Logit Analysis of Contingency Tables | 109 |
Chapter 5 Multinomial Logit Analysis | 139 |
Chapter 6 Logistic Regression for Ordered Categories | 159 |
Chapter 7 Discrete Choice Analysis | 189 |
| 217 | |
Chapter 9 Regression for Count Data | 265 |
Chapter 10 Loglinear Analysis of Contingency Tables | 291 |
| 325 | |
| 331 | |
Other editions - View all
Logistic Regression Using SAS: Theory and Application, Second Edition Paul D. Allison Limited preview - 2012 |
Logistic Regression Using SAS(R): Theory and Application, Second Edition Paul Allison No preview available - 2018 |
Common terms and phrases
Allison Analysis of Maximum binary logit blackd Chapter ChiSq Intercept CLASS variables 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 Error dichotomous effect equation Error Chi-Square Pr Estimate Error Chi-Square Estimates Standard Wald example explanatory variables frequencies GLIMMIX Here’s interaction Likelihood Estimates Standard Likelihood Parameter Estimates likelihood ratio Logistic Regression loglinear model Maximum Likelihood Estimates Maximum Likelihood Parameter method mixed model MODEL statement multinomial logit multinomial logit model null hypothesis odds ratio Odds Ratio Estimates option overdispersion p-values Parameter DF Estimate Paul Paul D 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 Square Pr standard errors Standard Wald Parameter Theory and Application Value/DF variance Wald Parameter DF wallet whitvic