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Logistic Regression

This volume helps readers understand the intuitive logic behind logistic regression through nontechnical language and simple examples. The Second Edition presents results from several statistical packages to help interpret the meaning of logistic regression coefficients, presents more detail on variations in logistic regression for multicategory outcomes, and describes some potential problems in interpreting logistic regression coefficients. A companion website includes the three data sets and Stata, SPSS, and R commands needed to reproduce all the tables and figures in the book. Finally, the Appendix reviews the meaning of logarithms, and helps readers understand the use of logarithms in logistic regression as well as in other types of models.

Series Editor’s Introduction
About the Author
Chapter 1: The Logic of Logistic Regression
Regression With a Binary Dependent Variable

Transforming Probabilities Into Logits

Linearizing the Nonlinear


Chapter 2: Interpreting Logistic Regression Coefficients
Logged Odds



Standardized Coefficients

Group and Model Comparisons of Logistic Regression Coefficients


Chapter 3: Estimation and Model Fit
Maximum Likelihood Estimation

Tests of Significance Using Log Likelihood Values

Model Goodness of Fit


Chapter 4: Probit Analysis
Another Way to Linearize the Nonlinear

The Probit Transformation


Maximum Likelihood Estimation


Chapter 5: Ordinal and Multinomial Logistic Regression
Ordinal Logistic Regression

Multinomial Logistic Regression


Appendix: Logarithms
The Logic of Logarithms

Properties of Logarithms

Natural Logarithms




Sage College Publishing

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