Ebook: Applied Logistic Regression, Second Edition

From the reviews of the First Edition.

"An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."
-Choice

"Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent."
-Contemporary Sociology

"An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."
-The Statistician

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.

Content:
Chapter 1 Introduction to the Logistic Regression Model (pages 1–30):
Chapter 2 Multiple Logistic Regression (pages 31–46):
Chapter 3 Interpretation of the Fitted Logistic Regression Model (pages 47–90):
Chapter 4 Model?Building Strategies and Methods for Logistic Regression (pages 91–142):
Chapter 5 Assessing the Fit of the Model (pages 143–202):
Chapter 6 Application of Logistic Regression with Different Sampling Models (pages 203–222):
Chapter 7 Logistic Regression for Matched Case?Control Studies (pages 223–259):
Chapter 8 Special Topics (pages 260–351):