Logistic Regression: A Self-Learning Text, 2/e

Bernhardt Fudyma Design Group

  • 出版商: Watson-Guptill Pubns
  • 出版日期: 1997-11-01
  • 售價: $1,400
  • 貴賓價: 9.8$1,372
  • 語言: 英文
  • 頁數: 272
  • 裝訂: Hardcover
  • ISBN: 0823055531
  • ISBN-13: 9780387953977
  • 相關分類: 地理資訊系統

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This is the second edition of this text on logistic regression methods. As in the first edition, each chapter contains a presentation of its topic in "lecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture-book" has a sequence of illustrations and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented. This second edition includes five new chapters and an appendix. The new chapters are: Chapter 9. Polytomous Logistic Regression Chapter 10. Ordinal Logistic Regression Chapter 11. Logistic Regression for Correlated Data Chapter 12. GEE Examples Chapter 13. Other Approaches for Analysis of Correlated Data Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11-13 extend logistic regression to generalized estimating equations (GEE) and other methods for analyzing correlated response data. The appendix "Computer Programs for Logistic Regression" provides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The software packages considered are SAS Version 8.0, SPSS Version 10.0 and STATA Version 7.0.


Table of Contents

Introduction to Logistic Regression * Important Special Cases of the Logistical Model * Computing the Odds Ration in Logistic Regression * Maximum Likelihood Techniques: An Overview * Statistical Inference Using Maximum Likelihood Techniques * Modeling Strategy Guidelines * Modeling Strategy for Assessing Interaction and Confounding * Analysis of Matched Data Using Logistic Regression * Polytomous Logistic Regression * Ordinal Logistic Regression * Logistic Regression for Correlated Data * GEE Examples * Other Approaches for Analysis of Correlated Data