Applied Logistic Regression
eBook - ePub

Applied Logistic Regression

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

About this book

A new edition of the definitive guide to logistic regression modeling for health science and other applications

This thoroughly expanded Third Edition 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.

Applied Logistic Regression, 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:

  • A chapter on the analysis of correlated outcome data
  • A wealth of additional material for topics ranging from Bayesian methods to assessing model fit
  • Rich data sets from real-world studies that demonstrate each method under discussion
  • Detailed examples and interpretation of the presented results as well as exercises throughout

Applied Logistic Regression, 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.

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Yes, you can access Applied Logistic Regression by David W. Hosmer, Jr.,Stanley Lemeshow,Rodney X. Sturdivant in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2013
Print ISBN
9780470582473
eBook ISBN
9781118548356

Table of contents

  1. Cover
  2. Series
  3. Title Page
  4. Copyright
  5. Dedication
  6. Preface to the Third Edition
  7. Chapter 1: Introduction to the Logistic Regression Model
  8. Chapter 2: The Multiple Logistic Regression Model
  9. Chapter 3: Interpretation of the Fitted Logistic Regression Model
  10. Chapter 4: Model-Building Strategies and Methods for Logistic Regression
  11. Chapter 5: Assessing the Fit of the Model
  12. Chapter 6: Application of Logistic Regression with Different Sampling Models
  13. Chapter 7: Logistic Regression for Matched Case-Control Studies
  14. Chapter 8: Logistic Regression Models for Multinomial and Ordinal Outcomes
  15. Chapter 9: Logistic Regression Models for the Analysis of Correlated Data
  16. Chapter 10: Special Topics
  17. References
  18. Index
  19. End User License Agreement