Applied Logistic Regression / Edition 2

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Overview

In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples—with extensive data sets available over the Internet.
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Editorial Reviews

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)

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)
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Product Details

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.

Multiple Logistic Regression.

Interpretation of the Fitted 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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