Advertisement

Logistic Regression

A Self-Learning Text

  • David G. Kleinbaum
  • Mitchel Klein

Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. David G. Kleinbaum, Mitchel Klein
    Pages 1-39
  3. David G. Kleinbaum, Mitchel Klein
    Pages 41-71
  4. David G. Kleinbaum, Mitchel Klein
    Pages 73-101
  5. David G. Kleinbaum, Mitchel Klein
    Pages 103-127
  6. David G. Kleinbaum, Mitchel Klein
    Pages 129-164
  7. David G. Kleinbaum, Mitchel Klein
    Pages 165-202
  8. David G. Kleinbaum, Mitchel Klein
    Pages 203-239
  9. David G. Kleinbaum, Mitchel Klein
    Pages 241-299
  10. David G. Kleinbaum, Mitchel Klein
    Pages 301-343
  11. David G. Kleinbaum, Mitchel Klein
    Pages 345-387
  12. David G. Kleinbaum, Mitchel Klein
    Pages 389-428
  13. David G. Kleinbaum, Mitchel Klein
    Pages 429-462
  14. David G. Kleinbaum, Mitchel Klein
    Pages 463-488
  15. David G. Kleinbaum, Mitchel Klein
    Pages 489-538
  16. David G. Kleinbaum, Mitchel Klein
    Pages 539-565
  17. David G. Kleinbaum, Mitchel Klein
    Pages 567-598
  18. Back Matter
    Pages 599-701

About this book

Introduction

This very popular textbook is now in its third edition. Whether students or working professionals, readers appreciate its unique "lecture book" format. They often say the book reads like they are listening to an outstanding lecturer. This edition includes three new chapters, an updated computer appendix, and an expanded section about modeling guidelines that consider causal diagrams.

Like previous editions, this textbook provides a highly readable description of fundamental and more advanced concepts and methods of logistic regression. It is suitable for researchers and statisticians in medical and other life sciences as well as academicians teaching second-level regression methods courses.

The new chapters are:

• Additional Modeling Strategy Issues, including strategy with several exposures, screening variables, collinearity, influential observations and multiple-testing

• Assessing Goodness to Fit for Logistic Regression

• Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves

The Computer Appendix provides step-by-step instructions for using STATA (version 10.0), SAS (version 9.2), and SPSS (version 16) for procedures described in the main text.

David Kleinbaum is Professor of Epidemiology at Emory University Rollins School of Public Health in Atlanta, Georgia. Dr. Kleinbaum is internationally known for his innovative textbooks and teaching on epidemiological methods, multiple linear regression, logistic regression, and survival analysis. He has taught more than 200 courses worldwide. The recipient of numerous teaching awards, he received the first Association of Schools of Public Health Pfizer Award for Distinguished Career Teaching in 2005.

Mitchel Klein is Research Assistant Professor with a joint appointment in the Environmental and Occupational Health Department and the Epidemiology Department at Emory University Rollins School of Public Health. He has successfully designed and taught epidemiologic methods physicians at Emory’s Master of Science in Clinical Research Program. Dr. Klein is co-author with Dr. Kleinbaum of the second edition of Survival Analysis-A Self-Learning Text.

Keywords

Computerassistierte Detektion Likelihood Logistic Regression SAS SPSS Statistical Inference best fit

Authors and affiliations

  • David G. Kleinbaum
    • 1
  • Mitchel Klein
    • 2
  1. 1.Rollins School of Public HealthEmory UniversityAtlantaUSA
  2. 2.Rollins School of Public Health, Dept. EpidemiologyEmory UniversityAtlantaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-1742-3
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4419-1741-6
  • Online ISBN 978-1-4419-1742-3
  • Series Print ISSN 1431-8776
  • Series Online ISSN 2197-5671
  • Buy this book on publisher's site