O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Logistic Regression Using SAS, 2nd Edition

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 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.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Contents
  5. Preface
  6. Chapter 1 - Introduction
    1. 1.1 - What This Book Is About
    2. 1.2 - What This Book Is Not About
    3. 1.3 - What You Need to Know
    4. 1.4 - Computing
    5. 1.5 - References
  7. Chapter 2 - Binary Logistic Regression with PROC LOGISTIC: Basics
    1. 2.1 - Introduction
    2. 2.2 - Dichotomous Dependent Variables: Example
    3. 2.3 - Problems with Ordinary Linear Regression
    4. 2.4 - Odds and Odds Ratios
    5. 2.5 - The Logistic Regression Model
    6. 2.6 - Estimation of the Logistic Model: General Principles
    7. 2.7 - Maximum Likelihood Estimation with PROC LOGISTIC
    8. 2.8 - Interpreting Coefficients
    9. 2.9 - CLASS Variables
    10. 2.10 - Multiplicative Terms in the MODEL Statement
  8. Chapter 3 - Binary Logistic Regression: Details and Options
    1. 3.1 - Introduction
    2. 3.2 - Confidence Intervals
    3. 3.3 - Details of Maximum Likelihood Estimation
    4. 3.4 - Convergence Problems
    5. 3.5 - Multicollinearity
    6. 3.6 - Goodness-of-Fit Statistics
    7. 3.7 - Statistics Measuring Predictive Power
    8. 3.8 - ROC Curves
    9. 3.9 - Predicted Values, Residuals, and Influence Statistics
    10. 3.10 - Latent Variables and Standardized Coefficients
    11. 3.11 - Probit and Complementary Log-Log Models
    12. 3.12 - Unobserved Heterogeneity
    13. 3.13 - Sampling on the Dependent Variable
    14. 3.14 - Plotting Effects of Predictor Variables
  9. Chapter 4 - Logit Analysis of Contingency Tables
    1. 4.1 - Introduction
    2. 4.2 - A Logit Model for a 2 × 2 Table
    3. 4.3 - A Three-Way Table
    4. 4.4 - A Four-Way Table
    5. 4.5 - A Four-Way Table with Ordinal Explanatory Variables
    6. 4.6 - Overdispersion
  10. Chapter 5 - Multinomial Logit Analysis
    1. 5.1 - Introduction
    2. 5.2 - Example
    3. 5.3 - A Model for Three Categories
    4. 5.4 - Estimation with PROC LOGISTIC
    5. 5.5 - Estimation with a Binary Logit Procedure
    6. 5.6 - General Form of the Model
    7. 5.7 - Contingency Table Analysis
    8. 5.8 - Problems of Interpretation
  11. Chapter 6 - Logistic Regression for Ordered Categories
    1. 6.1 - Introduction
    2. 6.2 - Cumulative Logit Model: Example
    3. 6.3 - Cumulative Logit Model: Explanation
    4. 6.4 - Cumulative Logit Model: Practical Considerations
    5. 6.5 - Cumulative Logit Model: Contingency Tables
    6. 6.6 - Adjacent Categories Model
    7. 6.7 - Continuation Ratio Model
  12. Chapter 7 - Discrete Choice Analysis
    1. 7.1 - Introduction
    2. 7.2 - Chocolate Example
    3. 7.3 - Model and Estimation
    4. 7.4 - Travel Example
    5. 7.5 - Other Applications
    6. 7.6 - Ranked Data
    7. 7.7 - More Advanced Models with PROC MDC
  13. Chapter 8 - Logit Analysis of Longitudinal and Other Clustered Data
    1. 8.1 - Introduction
    2. 8.2 - Longitudinal Example
    3. 8.3 - Robust Standard Errors
    4. 8.4 - GEE Estimation with PROC GENMOD
    5. 8.5 - Mixed Models with GLIMMIX
    6. 8.6 - Fixed-Effects with Conditional Logistic Regression
    7. 8.7 - Postdoctoral Training Example
    8. 8.8 - Matching
    9. 8.9 - Comparison of Methods
    10. 8.10 - A Hybrid Method
  14. Chapter 9 - Regression for Count Data
    1. 9.1 - Introduction
    2. 9.2 - The Poisson Regression Model
    3. 9.3 - Scientific Productivity Example
    4. 9.4 - Overdispersion
    5. 9.5 - Negative Binomial Regression
    6. 9.6 - Adjustment for Varying Time Spans
    7. 9.7 - Zero-Inflated Models
  15. Chapter 10 - Loglinear Analysis of Contingency Tables
    1. 10.1 - Introduction
    2. 10.2 - A Loglinear Model for a 2 × 2 Table
    3. 10.3 - Loglinear Models for a Four-Way Table
    4. 10.4 - Fitting the Adjacent Categories Model as a Loglinear Model
    5. 10.5 - Loglinear Models for Square, Ordered Tables
    6. 10.6 - Marginal Tables
    7. 10.7 - The Problem of Zeros
    8. 10.8 - GENMOD versus CATMOD
  16. References
  17. Index