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Logistic Regression A Self-Learning Text

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ISBN-10: 0387953973

ISBN-13: 9780387953977

Edition: 2nd 2005 (Revised)

Authors: David G. Kleinbaum, Mitchell Klein, E. Rihl Pryor

List price: $109.00
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Description:

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.
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Book details

List price: $109.00
Edition: 2nd
Copyright year: 2005
Publisher: Springer
Publication date: 10/25/2005
Binding: Hardcover
Pages: 513
Size: 8.50" wide x 9.50" long x 1.25" tall
Weight: 2.486
Language: English

Preface
Acknowledgments
Introduction to Logistic Regression
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Key Formulae
Practice Exercises
Test
Answers to Practice Exercises
Important Special Cases of the Logistic Model
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
Computing the Odds Ratio in Logistic Regression
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
Maximum Likelihood Techniques: An Overview
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
Statistical Inferences Using Maximum Likelihood Techniques
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
Modeling Strategy Guidelines
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
Modeling Strategy for Assessing Interaction and Confounding
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
Analysis of Matched Data Using Logistic Regression
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
Polytomous Logistic Regression
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
Ordinal Logistic Regression
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
Logistic Regression for Correlated Data: GEE
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
GEE Examples
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
Other Approaches for Analysis of Correlated Data
Introduction
Abbreviated Outline
Objectives
Presentation
Detailed Outline
Practice Exercises
Test
Answers to Practice Exercises
Computer Programs for Logistic Regression
Datasets
SAS
SPSS
Stata
Test Answers
Bibliography
Index