Description
Book Synopsisto Logistic Regression.- Important Special Cases of the Logistic Model.- Computing the Odds Ratio in Logistic Regression.- Maximum Likelihood Techniques: An Overview.- Statistical Inferences Using Maximum Likelihood Techniques.- Modeling Strategy Guidelines.- Modeling Strategy for Assessing Interaction and Confounding.- Additional Modeling Strategy Issues.- Assessing Goodness of Fit for Logistic Regression.- Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves.- Analysis of Matched Data Using Logistic Regression.- Polytomous Logistic Regression.- Ordinal Logistic Regression.- Logistic Regression for Correlated Data: GEE.- GEE Examples.- Other Approaches for Analysis of Correlated Data.
Trade ReviewFrom the reviews of the third edition:
“The third edition of this book continues the tradition of the authors of a two-column book that really does act as a self-learning text. The left-hand column is like a collection of PowerPoint slides, including generic-style computer output and diagrams to visualize the relationship between concepts. Each chapter contains about 10 exercises, some routine calculation and some asking for explanation of particular points. Answers are provided immediately. … The reference list includes about 40 items and has been updated to include publications up to 2008.” (Alice Richardson, International Statistical Review, Vol. 79 (2), 2011)
Table of Contentsto Logistic Regression.- Important Special Cases of the Logistic Model.- Computing the Odds Ratio in Logistic Regression.- Maximum Likelihood Techniques: An Overview.- Statistical Inferences Using Maximum Likelihood Techniques.- Modeling Strategy Guidelines.- Modeling Strategy for Assessing Interaction and Confounding.- Additional Modeling Strategy Issues.- Assessing Goodness of Fit for Logistic Regression.- Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves.- Analysis of Matched Data Using Logistic Regression.- Polytomous Logistic Regression.- Ordinal Logistic Regression.- Logistic Regression for Correlated Data: GEE.- GEE Examples.- Other Approaches for Analysis of Correlated Data.
