Applied Logistic Regression 3rd Edition PDF Download Ebook. David W. Hosmer Jr., Stanley Lemeshow and Rodney X. Sturdivant provide an easily accessible introduction to the logistic regression (LR) model and highlight the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.

This text 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 chapter on the analysis of correlated outcome data with wealth of additional material for topics ranging from Bayesian methods to assessing model fit.

Rich data sets from real-world studies demonstrate each method under discussion with detailed examples and interpretation of the presented results as well as exercises throughout. It 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.

Author shows how to model a binary outcome variable from a linear regression analysis point of view and develops the logistic regression model and describes its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariates. Following establishment of the model there is discussion of its interpretation, several data sets are the source of the examples and the exercises, and a number of software packages are used to analyze data sets, including BMDP, EGRET, GLIM, SAS, and SYSTAT.

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