Applied Logistic Regression, 3rd Edition (Wiley Series in Probability and Statistics) - Tapa dura

Acerca del autor

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

STANLEY LEMESHOW, PhD, is Professor of Biostatistics and Founding Dean of the College of Public Health at The Ohio State University, Columbus, Ohio.

RODNEY X. STURDIVANT, PhD, is Associate Professor and Founding Director of the Center for Data Analysis and Statistics at the United States Military Academy at West Point, New York.

De la contraportada

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.

De la solapa interior

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.