Applied Logistic Regression, Hardcover, 3 Edition by Hosmer Jr., David W.

Hardcover: 3 Edition
9780470582473
0470582472

Publication Date: 2013-04-01
Publisher: Wiley
Hardcover : 528 pages
Edition: 3 Edition
Author: Hosmer Jr., David W.
ISBN-10: 0470582472
ISBN-13: 9780470582473

Product Description 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. Review “In conclusion, the index was mercifully complete, and all items searched for were found (nice cross-referencing too) In summary: Highly recommended.” (Scientific Computing, 1 May 2013) From the Inside Flap A new edition of the definitive guide to logistic regression modeling for health science and other applications Praise for the Second Edition ". . . an excellent book that balances many objectives well. . . . Applied Logistic Regression is an ideal choice." —Technometrics ". . . it remains an extremely valuable text for everyone working or teaching in fields like epidemiology." —Statistics in Medicine 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. From the Back Cover A new edition of the definitive guide to logistic regression modeling for health science and other applications Praise for the Second Edition ". . . an excellent book that balances many objectives well. . . . Applied Logistic Regression is an ideal choice." ―Technometrics ". . . it remains an extremely valuable text for everyone working or teaching in fields like epidemiology." ―Statistics in Medicine 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 t