Cantitate/Preț
Produs
Total produse
Vezi coșul
Applied Logistic Regression Analysis de Scott Menard

Applied Logistic Regression Analysis

Autor Scott Menard
en Limba Engleză Hardback – 9 oct 2001
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.
Citește tot Restrânge

Preț: 64642 lei

Preț vechi: 76049 lei
-15%

Puncte Express: 970
Hardback 64642 lei
Electronic book text 11420 lei

Carte tipărită la comandă

Livrare economică 18 iunie-02 iulie

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă

Specificații

ISBN-13: 9781483347226
ISBN-10: 1483347222
Pagini: 120
Dimensiuni: 145 x 222 x 10 mm
Greutate: 0.29 kg
Editura: Sage Publications, Inc

Cuprins

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

Descriere

Descriere de la o altă ediție sau format:
The focus in thisSecond Editionis 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.