Logistic Regression Using SAS®: Theory and Application

Table of contents

1. Copyright
2. Acknowledgments
3. Introduction
   1. What This Book Is About
   2. What This Book Is Not About
   3. What You Need to Know
   4. Computing
   5. References
4. Binary Logit Analysis: Basics
   1. Introduction
   2. Dichotomous Dependent Variables: Example
   3. Problems with Ordinary Linear Regression
   4. Odds and Odds Ratios
   5. The Logit Model
   6. Estimation of the Logit Model: General Principles
   7. Maximum Likelihood Estimation with PROC LOGISTIC
   8. Maximum Likelihood Estimation with PROC GENMOD
   9. Interpreting Coefficients
5. Binary Logit Analysis: Details and Options
   1. Introduction
   2. Confidence Intervals
   3. Details of Maximum Likelihood Estimation
   4. Convergence Problems
   5. Multicollinearity
   6. Goodness-of-Fit Statistics
   7. Statistics Measuring Predictive Power
   8. Predicted Values, Residuals, and Influence Statistics
   9. Latent Variables and Standardized Coefficients
   10. Probit and Complementary Log-Log Models
   11. Unobserved Heterogeneity
   12. Sampling on the Dependent Variable
6. Logit Analysis of Contingency Tables
   1. Introduction
   2. A Logit Model for a 2 × 2 Table
   3. A Three-Way Table
   4. A Four-Way Table
   5. A Four-Way Table with Ordinal Explanatory Variables
   6. Overdispersion
7. Multinomial Logit Analysis
   1. Introduction
   2. Example
   3. A Model for Three Categories
   4. Estimation with CATMOD
   5. Estimation with a Binary Logit Procedure
   6. General Form of the Model
   7. Contingency Table Analysis
   8. CATMOD Coding of Categorical Variables
   9. Problems of Interpretation
8. Logit Analysis for Ordered Categories
   1. Introduction
   2. Cumulative Logit Model: Example
   3. Cumulative Logit Model: Explanation
   4. Cumulative Logit Model: Practical Considerations
   5. Cumulative Logit Model: Contingency Tables
   6. Adjacent Categories Model
   7. Continuation Ratio Model
9. Discrete Choice Analysis
   1. Introduction
   2. Chocolate Example
   3. Model and Estimation
   4. Travel Example
   5. Other Applications
   6. Ranked Data
10. Logit Analysis of Longitudinal and Other Clustered Data
    1. Introduction
    2. Longitudinal Example
    3. GEE Estimation
    4. Fixed-Effects with Conditional Logit Analysis
    5. Postdoctoral Training Example
    6. Matching
    7. Mixed Logit Models
    8. Comparison of Methods
    9. A Hybrid Method
11. Poisson Regression
    1. Introduction
    2. The Poisson Regression Model
    3. Scientific Productivity Example
    4. Overdispersion
    5. Negative Binomial Regression
    6. Adjustment for Varying Time Spans
12. Loglinear Analysis of Contingency Tables
    1. Introduction
    2. A Loglinear Model for a 2 × 2 Table
    3. Loglinear Models for a Four-Way Table
    4. Fitting the Adjacent Categories Model as a Loglinear Model
    5. Loglinear Models for Square, Ordered Tables
    6. Marginal Tables
    7. The Problem of Zeros
    8. GENMOD versus CATMOD
13. Appendix
    1. The ROBUST Macro
    2. The PENALTY Data Set: Outcomes for Murder Defendants
    3. The WALLET Data Set: Altruistic Behavior by College Students
    4. The TRAVEL Data Set: Transportation Choices to Australian Cities
    5. The JUDGERNK Data Set: Rankings of Seriousness of Murder Cases
    6. The PTSD Data Set: Psychological Distress among Fire Victims
    7. The POSTDOC Data Set: Postdoctoral Training among Biochemists
    8. The CASECONT Data Set: Women in Homeless Shelters
    9. The PROGNOSI Data Set: Physicians’ Utterances about Prognosis
14. Books Available from SAS® Press
    1. JMP® Books
15. Wiley Series in Probability and Statistics
16. Wiley Series in Probability and Statistics
17. References
18. Index