Applied Logistic Regression / Edition 3

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Overview

A new edition of the definitive guide to logistic regression modeling for health science and other applications

This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.

Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:

• A chapter on the analysis of correlated outcome data

• A wealth of additional material for topics ranging from Bayesian methods to assessing model fit

• Rich data sets from real-world studies that demonstrate each method under discussion

• Detailed examples and interpretation of the presented results as well as exercises throughout

Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.

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Editorial Reviews

Booknews
A textbook for part of a graduate survey course, courses of a quarter or semester, and focused short courses for working professionals. Assuming a solid foundation in linear regression methodology and contingency table analysis, biostaticians Hosmer (U. of Massachusetts- Amherst) and Lemeshow (Ohio State U.) introduce the logistic regression model and its use in methods for modeling the relationship between a categorical outcome variable and a set of covariates. The first edition appeared about a decade ago, and the second incorporates theoretical and computational developments since then. Annotation c. Book News, Inc., Portland, OR (booknews.com)
From the Publisher
"This well written, organized, comprehensive, and useful book will be appreciated by graduate students and researchers." (Journal of Statistical Computation and Simulation, January 2006)

"...the revised text continues to provide a focused introduction to the logistic regression model and its use in methods for modelling..." (Short Book Reviews, Vol. 21, No. 2, August 2001)

"In this revised and updated edition of the popular test, the authors incorporate theoretical and computing advances from the last decade." (Journal of the American Statistical Association, September 2001)

"...an excellent book that balances many objectives well.... All statistical practitioners...can benefit from this book...Applied Logistic Regression is an ideal choice." (Technometrics, February 2002)

"...a focused introduction to the logistic regression model and its use in methods for modeling the relationship between a categorical outcome variable and a set of covariates." (Zentralblatt MATH, Vol. 967, 2001/17)

"...it remains an extremely valuable text for everyone working or teaching in fields like epidemiology..." (Statistics in Medicine, No.21, 2002)

"...The book is a classic, extremely well written, and it includes a variety of software packages and real examples...." (The Statistician, Vol. 51, No.2, 2002)

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Product Details

  • ISBN-13: 9780470582473
  • Publisher: Wiley
  • Publication date: 4/8/2013
  • Series: Wiley Series in Probability and Statistics Series , #398
  • Edition description: New Edition
  • Edition number: 3
  • Pages: 528
  • Sales rank: 298,704
  • Product dimensions: 6.40 (w) x 9.30 (h) x 1.30 (d)

Meet the Author

DAVID W. HOSMER, PhD, is Professor of Biostatistics at the School of Public Health and Health Sciences at the University of Massachusetts at Amherst.

STANLEY LEMESHOW, PhD, is Professor of Biostatistics and Director of the Biostatistics Program at The Ohio State University.

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Table of Contents

Introduction to the Logistic Regression Model.
The Multiple Logistic Regression Model.
Interpretation of the Coefficients of the Logistic Regression Model.
Model-Building Strategies and Methods for Logistic Regression.
Assessing the Fit of the Model.
Application of Logistic Regression with Different Sampling Models.
Logistic Regression for Matched Case-Control Studies.
Special Topics.
References.
Index.
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