In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples—with extensive data sets available over the Internet.
A textbook for part of a graduate survey course, courses of a quarter or semester, and focused short courses for working professionals. Assuming a solid foundation in linear regression methodology and contingency table analysis, biostaticians Hosmer (U. of Massachusetts- Amherst) and Lemeshow (Ohio State U.) introduce the logistic regression model and its use in methods for modeling the relationship between a categorical outcome variable and a set of covariates. The first edition appeared about a decade ago, and the second incorporates theoretical and computational developments since then. Annotation c. Book News, Inc., Portland, OR (booknews.com)
From the Publisher
“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)
Introduction to the Logistic Regression Model.
The Multiple Logistic Regression Model.
Interpretation of the Coefficients of the Logistic Regression Model.
Model-Building Strategies and Methods for Logistic Regression.
Assessing the Fit of the Model.
Application of Logistic Regression with Different Sampling Models.
Logistic Regression for Matched Case-Control Studies.