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Logistic Regression
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Logistic Regression
A Primer

Second Edition


October 2020 | 152 pages | SAGE Publications, Inc

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
 
Preface
 
Acknowledgments
 
About the Author
 
Chapter 1: The Logic of Logistic Regression
Regression With a Binary Dependent Variable

 
Transforming Probabilities Into Logits

 
Linearizing the Nonlinear

 
Summary

 
 
Chapter 2: Interpreting Logistic Regression Coefficients
Logged Odds

 
Odds

 
Probabilities

 
Standardized Coefficients

 
Group and Model Comparisons of Logistic Regression Coefficients

 
Summary

 
 
Chapter 3: Estimation and Model Fit
Maximum Likelihood Estimation

 
Tests of Significance Using Log Likelihood Values

 
Model Goodness of Fit

 
Summary

 
 
Chapter 4: Probit Analysis
Another Way to Linearize the Nonlinear

 
The Probit Transformation

 
Interpretation

 
Maximum Likelihood Estimation

 
Summary

 
 
Chapter 5: Ordinal and Multinomial Logistic Regression
Ordinal Logistic Regression

 
Multinomial Logistic Regression

 
Summary

 
 
Notes
 
Appendix: Logarithms
The Logic of Logarithms

 
Properties of Logarithms

 
Natural Logarithms

 
Summary

 
 
References
 
Index

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