Book Description
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 realworld 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, discretechoice 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.Table of Contents
 Cover Page
 Title Page
 Copyright Page
 Contents
 Preface
 Chapter 1  Introduction

Chapter 2  Binary Logistic Regression with PROC LOGISTIC: Basics
 2.1  Introduction
 2.2  Dichotomous Dependent Variables: Example
 2.3  Problems with Ordinary Linear Regression
 2.4  Odds and Odds Ratios
 2.5  The Logistic Regression Model
 2.6  Estimation of the Logistic Model: General Principles
 2.7  Maximum Likelihood Estimation with PROC LOGISTIC
 2.8  Interpreting Coefficients
 2.9  CLASS Variables
 2.10  Multiplicative Terms in the MODEL Statement

Chapter 3  Binary Logistic Regression: Details and Options
 3.1  Introduction
 3.2  Confidence Intervals
 3.3  Details of Maximum Likelihood Estimation
 3.4  Convergence Problems
 3.5  Multicollinearity
 3.6  GoodnessofFit Statistics
 3.7  Statistics Measuring Predictive Power
 3.8  ROC Curves
 3.9  Predicted Values, Residuals, and Influence Statistics
 3.10  Latent Variables and Standardized Coefficients
 3.11  Probit and Complementary LogLog Models
 3.12  Unobserved Heterogeneity
 3.13  Sampling on the Dependent Variable
 3.14  Plotting Effects of Predictor Variables
 Chapter 4  Logit Analysis of Contingency Tables
 Chapter 5  Multinomial Logit Analysis
 Chapter 6  Logistic Regression for Ordered Categories
 Chapter 7  Discrete Choice Analysis

Chapter 8  Logit Analysis of Longitudinal and Other Clustered Data
 8.1  Introduction
 8.2  Longitudinal Example
 8.3  Robust Standard Errors
 8.4  GEE Estimation with PROC GENMOD
 8.5  Mixed Models with GLIMMIX
 8.6  FixedEffects with Conditional Logistic Regression
 8.7  Postdoctoral Training Example
 8.8  Matching
 8.9  Comparison of Methods
 8.10  A Hybrid Method
 Chapter 9  Regression for Count Data

Chapter 10  Loglinear Analysis of Contingency Tables
 10.1  Introduction
 10.2  A Loglinear Model for a 2 × 2 Table
 10.3  Loglinear Models for a FourWay Table
 10.4  Fitting the Adjacent Categories Model as a Loglinear Model
 10.5  Loglinear Models for Square, Ordered Tables
 10.6  Marginal Tables
 10.7  The Problem of Zeros
 10.8  GENMOD versus CATMOD
 References
 Index
Product Information
 Title: Logistic Regression Using SAS, 2nd Edition
 Author(s):
 Release date: March 2012
 Publisher(s): SAS Institute
 ISBN: 9781607649953