Title: Applied Logistic Regression Volume:  
Author(s): David W. Hosmer, Stanley Lemeshow, Rodney X. Sturdivant (auth.), Walter A. Shewhart, Samuel S. Wilks (eds.)
Series: Wiley Series in Probability and Statistics Periodical:  
Publisher: Wiley City:  
Year: 2013 Edition: 3rd
Language: English Pages: 528
ISBN: 9780470582473, 9781118548387, 0470582472 ID: 1015725
Time added: 2013-09-10 20:49:15 Time modified: 2013-09-11 11:22:35
Library:  Library issue: 0
Size: 5 MB (4924097 bytes) Extension: pdf

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 dataA wealth of additional material for topics ranging from Bayesian methods to assessing model fitRich data sets from real-world studies that demonstrate each method under discussionDetailed 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.

Content:
Chapter 1 Introduction to the Logistic Regression Model (pages 1–33):
Chapter 2 The Multiple Logistic Regression Model (pages 35–47):
Chapter 3 Interpretation of the Fitted Logistic Regression Model (pages 49–88):
Chapter 4 Model‐Building Strategies and Methods for Logistic Regression (pages 89–151):
Chapter 5 Assessing the Fit of the Model (pages 153–225):
Chapter 6 Application of Logistic Regression with Different Sampling Models (pages 227–242):
Chapter 7 Logistic Regression for Matched Case‐Control Studies (pages 243–268):
Chapter 8 Logistic Regression Models for Multinomial and Ordinal Outcomes (pages 269–311):
Chapter 9 Logistic Regression Models for the Analysis of Correlated Data (pages 313–376):
Chapter 10 Special Topics (pages 377–457):