Logistic Regression A Self-Learning Text /

• Κύριοι συγγραφείς: Kleinbaum, David G. (Συγγραφέας), Klein, Mitchel (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: New York, NY : Springer New York, 2002.
• Έκδοση: Second Edition.
• Σειρά: Statistics for Biology and Health,
• Θέματα: Statistics.; Epidemiology.; Statistics for Life Sciences, Medicine, Health Sciences.; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.

 This is the second edition of this text on logistic regression methods, ori- nally published in 1994. 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 illust- tions 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 has expanded the first edition by adding five new ch- ters and a new appendix. The five new chapters are Chapter 9. Polytomous Logistic Regression Chapter 10. Ordinal Logistic Regression Chapter 11. Logistic Regression for Correlated Data: GEE 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 gen- alized estimating equations (GEE) and other methods for analyzing cor- lated response data. The appendix is titled “Computer Programs for Logistic Regression” and p- vides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The so- ware packages considered are SAS Version 8.0, SPSS Version 10.0, and STATA Version 7.0. 

 to Logistic Regression -- Important Special Cases of the Logistic Model -- Computing the Odds Ratio in Logistic Regression -- Maximum Likelihood Techniques: An Overview -- Statistical Inferences 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 Regresion for Correlated Data: GEE -- GEE Examples -- Other Approaches for Analysis of Correlated Data.