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Applied Logistic Regression, Textbook and Solutions Manual / Edition 2

Hardcover (Print)
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Overview

From the reviews of the First Edition.

"An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."
Choice

"Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent."
Contemporary Sociology

"An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."
The Statistician

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

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.
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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