Logistic Regression: A Self-Learning Text / Edition 2

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Overview

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.

This book contains no illustrations.

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Editorial Reviews

Viswanathan Ramakrishnan
This book on logistic regression, written by a widely published statistician, covers most of the general concepts on the topic. The "lecture-book" style of presentation is unique and provides a useful tool for teaching. The purpose is to provide a self-learning book for those who do not have the time or opportunity to participate in a formal course on logistic regression. This book will be most useful for students who have taken some preliminary statistics classes with or without previous exposure to the topic of logistic regression. The book does not require a strong background in calculus or mathematical statistics. The lecture-book format is interesting even though it takes a while to get used to. At first a reader might ignore the left column in each page completely and read the right column, which follows a standard textbook format. However, once one gets used to using the examples and the lecture-style presentation in the left columns, it proves to be very helpful. This book is very well written and provides excellent explanations of the major concepts in the logistic regression methodology. An audio version of the right columns of the book is available. It would be interesting to see how the numerous formulas are conveyed verbally. The major limitation of the book is that it does not provide any information on how one could use software (such as SAS, SPSS, BMDP, etc.) to apply the methods to analyze data. Inclusion of such information is crucial in this type of book, especially because logistic regression analysis involves nonlinear estimation through iterative procedures and hence cannot be performed using hand calculators. I recommend this book for a course for nonstatisticsmajors that has logistic regression as its major focus.
From The Critics
Reviewer: Viswanathan Ramakrishnan, PhD (University of Illinois at Chicago)
Description: This book on logistic regression, written by a widely published statistician, covers most of the general concepts on the topic. The "lecture-book" style of presentation is unique and provides a useful tool for teaching.
Purpose: The purpose is to provide a self-learning book for those who do not have the time or opportunity to participate in a formal course on logistic regression.
Audience: This book will be most useful for students who have taken some preliminary statistics classes with or without previous exposure to the topic of logistic regression. The book does not require a strong background in calculus or mathematical statistics.
Features: The lecture-book format is interesting even though it takes a while to get used to. At first a reader might ignore the left column in each page completely and read the right column, which follows a standard textbook format. However, once one gets used to using the examples and the lecture-style presentation in the left columns, it proves to be very helpful.
Assessment: This book is very well written and provides excellent explanations of the major concepts in the logistic regression methodology. An audio version of the right columns of the book is available. It would be interesting to see how the numerous formulas are conveyed verbally. The major limitation of the book is that it does not provide any information on how one could use software (such as SAS, SPSS, BMDP, etc.) to apply the methods to analyze data. Inclusion of such information is crucial in this type of book, especially because logistic regression analysis involves nonlinear estimation through iterative procedures and hence cannot be performed using hand calculators. I recommend this book for a course for nonstatistics majors that has logistic regression as its major focus.
From the Publisher
From 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)


5 Stars! from Doody
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Product Details

  • ISBN-13: 9780387953977
  • Publisher: Springer-Verlag New York, LLC
  • Publication date: 8/28/2002
  • Series: Statistics for Biology and Health Series
  • Edition description: REV
  • Edition number: 2
  • Pages: 513
  • Product dimensions: 8.10 (w) x 9.30 (h) x 1.30 (d)

Table of Contents

Preface
Acknowledgments
Ch. 1 Introduction to Logistic Regression 1
Ch. 2 Important Special Cases of the Logistic Model 39
Ch. 3 Computing the Odds Ratio in Logistic Regression 73
Ch. 4 Maximum Likelihood Techniques: An Overview 101
Ch. 5 Statistical Inferences Using Maximum Likelihood Techniques 125
Ch. 6 Modeling Strategy Guidelines 161
Ch. 7 Modeling Strategy for Assessing Interaction and Confounding 191
Ch. 8 Analysis of Matched Data Using Logistic Regression 227
Ch. 9 Polytomous Logistic Regression 267
Ch. 10 Ordinal Logistic Regression 301
Ch. 11 Logistic Regression for Correlated Data: GEE 327
Ch. 12 GEE Examples 377
Ch. 13 Other Approaches for Analysis of Correlated Data 405
App Computer Programs for Logistic Regression 437
Test Answers 487
Bibliography 503
Index 507
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