You are here

In observance of the 2024 holiday season, Sage offices will be closed Monday December 23rd through Wednesday January 1st. Normal operations, including shipping for orders placed during the closure, will resume on Thursday January 2nd. For technical support during this time, please visit our technical support page for assistance options. 

We wish you a wonderful holiday season. Thank you. 

Unfortunately, as of 1 January 2020 SAGE Ltd is no longer able to support sales of electronically supplied services to Taiwan customers that are not Taiwan VAT registered. We apologise for any inconvenience. For more information or to place a print-only order, please contact uk.customerservices@sagepub.co.uk.

Applied Logistic Regression Analysis
Share
Share

Applied Logistic Regression Analysis

Second Edition
  • Scott Menard - Sam Houston State University, USA, University of Colorado, USA


October 2001 | 128 pages | SAGE Publications, Inc

The focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered. The book includes detailed discussions of goodness of fit, indices of predictive efficiency, and standardized logistic regression coefficients, and examples using SAS and SPSS are included.

  • More detailed consideration of grouped as opposed to case-wise data throughout the book
  • Updated discussion of the properties and appropriate use of goodness of fit measures, R-square analogues, and indices of predictive efficiency
  • Discussion of the misuse of odds ratios to represent risk ratios, and of over-dispersion and under-dispersion for grouped data

Updated coverage of unordered and ordered polytomous logistic regression models. 


Learn more about "The Little Green Book" - QASS Series! Click Here


 
Series Editor's Introduction
 
Author's Introduction to the Second Edition
 
1. Linear Regression and Logistic Regression Model
 
2. Summary Statistics for Evaluating the Logistic Regression Model
 
3. Interpreting the Logistic Regression Coefficients
 
4. An Introduction to Logistic Regression Diagnosis
 
Ch 5. Polytomous Logistic Regression and Alternatives to Logistic Regression
 
6. Notes
 
Appendix A
 
References
 
Tables
 
Figures
Key features
  • More detailed consideration of grouped as opposed to case-wise data throughout the book
  • Updated discussion of the properties and appropriate use of goodness of fit measures, R-square analogues, and indices of predictive efficiency
  • Discussion of the misuse of odds ratios to represent risk ratios, and of over-dispersion and under-dispersion for grouped data
  • Updated coverage of unordered and ordered polytomous logistic regression models. 

Sage College Publishing

You can purchase or sample this product on our Sage College Publishing site:

Go To College Site

This title is also available on SAGE Research Methods, the ultimate digital methods library. If your library doesn’t have access, ask your librarian to start a trial.