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by Mitchel Klein and David G. Kleinbaum

Edition: 2ND 03Copyright: 2003

Publisher: Springer-Verlag New York

Published: 2003

International: No

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This is the second edition of this text on logistic regression methods. As in the first edition, each chapter contains a presentation of its topic in "lecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture-book" has a sequence of illustrations and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented.

This second edition includes five new chapters and an appendix. The new chapters are:

Chapter 9. Polytomous Logistic Regression

Chapter 10. Ordinal Logistic Regression

Chapter 11. Logistic Regression for Correlated Data

Chapter 12. GEE Examples

Chapter 13. Other Approaches for Analysis of Correlated Data

Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11-13 extend logistic regression to generalized estimating equations (GEE) and other methods for analyzing correlated response data.

The appendix "Computer Programs for Logistic Regression" provides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The software packages considered are SAS Version 8.0, SPSS Version 10.0 and STATA Version 7.0.

**Kleinbaum, David G. : Emory University, Atlanta, GA **

Klein, Mitchel : Emory University, Atlanta, GA

Introduction to Logistic Regression

Important Special Cases of the Logistical Model

Computing the Odds Ration in Logistic Regression

Maximum Likelihood Techniques: An Overview

Statistical Inference Using Maximum Likelihood Techniques

Modeling Strategy Guidelines

Modeling Strategy for Assessing Interaction and Confounding

Analysis of Matched Data Using Logistic Regression

Polytomous Logistic Regression

Ordinal Logistic Regression

Logistic Regression for Correlated Data

GEE Examples

Other Approaches for Analysis of Correlated Data

Summary

This is the second edition of this text on logistic regression methods. As in the first edition, each chapter contains a presentation of its topic in "lecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture-book" has a sequence of illustrations and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented.

This second edition includes five new chapters and an appendix. The new chapters are:

Chapter 9. Polytomous Logistic Regression

Chapter 10. Ordinal Logistic Regression

Chapter 11. Logistic Regression for Correlated Data

Chapter 12. GEE Examples

Chapter 13. Other Approaches for Analysis of Correlated Data

Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11-13 extend logistic regression to generalized estimating equations (GEE) and other methods for analyzing correlated response data.

The appendix "Computer Programs for Logistic Regression" provides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The software packages considered are SAS Version 8.0, SPSS Version 10.0 and STATA Version 7.0.

Author Bio

**Kleinbaum, David G. : Emory University, Atlanta, GA **

Klein, Mitchel : Emory University, Atlanta, GA

Table of Contents

Introduction to Logistic Regression

Important Special Cases of the Logistical Model

Computing the Odds Ration in Logistic Regression

Maximum Likelihood Techniques: An Overview

Statistical Inference Using Maximum Likelihood Techniques

Modeling Strategy Guidelines

Modeling Strategy for Assessing Interaction and Confounding

Analysis of Matched Data Using Logistic Regression

Polytomous Logistic Regression

Ordinal Logistic Regression

Logistic Regression for Correlated Data

GEE Examples

Other Approaches for Analysis of Correlated Data

Publisher Info

Publisher: Springer-Verlag New York

Published: 2003

International: No

Published: 2003

International: No

This is the second edition of this text on logistic regression methods. As in the first edition, each chapter contains a presentation of its topic in "lecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture-book" has a sequence of illustrations and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented.

This second edition includes five new chapters and an appendix. The new chapters are:

Chapter 9. Polytomous Logistic Regression

Chapter 10. Ordinal Logistic Regression

Chapter 11. Logistic Regression for Correlated Data

Chapter 12. GEE Examples

Chapter 13. Other Approaches for Analysis of Correlated Data

Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11-13 extend logistic regression to generalized estimating equations (GEE) and other methods for analyzing correlated response data.

The appendix "Computer Programs for Logistic Regression" provides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The software packages considered are SAS Version 8.0, SPSS Version 10.0 and STATA Version 7.0.

**Kleinbaum, David G. : Emory University, Atlanta, GA **

Klein, Mitchel : Emory University, Atlanta, GA

Important Special Cases of the Logistical Model

Computing the Odds Ration in Logistic Regression

Maximum Likelihood Techniques: An Overview

Statistical Inference Using Maximum Likelihood Techniques

Modeling Strategy Guidelines

Modeling Strategy for Assessing Interaction and Confounding

Analysis of Matched Data Using Logistic Regression

Polytomous Logistic Regression

Ordinal Logistic Regression

Logistic Regression for Correlated Data

GEE Examples

Other Approaches for Analysis of Correlated Data