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Applied Logistic Regression Analysis
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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. 


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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. 

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