Applied Logistic Regression

<b>A new edition of the definitive guide to logistic regression modeling</b> <b>for health science and other applications</b> <p>This thoroughly expanded <i>Third Edition</i> 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.</p> <p><i>Applied Logistic Regression</i>, 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:</p> <ul> <li>A chapter on the analysis of correlated outcome data</li> <li>A wealth of additional material for topics ranging from Bayesian methods to assessing model fit</li> <li>Rich data sets from real-world studies that demonstrate each method under discussion</li> <li>Detailed examples and interpretation of the presented results as well as exercises throughout</li> </ul> <p><i>Applied Logistic Regression</i>, 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.</p>