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Logistic Regression: A Primer

Author:
Fred C. Pampel
Publisher: Sage
Copyright: 2000
ISBN-13: 978-0-7619-2010-6
Pages: 86; paperback
Price: $17.75

Comment from the Stata technical group

This monograph provides an excellent introduction to logistic regression from first principles. It is an ideal tutorial for those who are familiar with standard linear regression and wish to branch out for the first time into more complex generalized linear models, for which logistic regression (regression with a binary response) is a good starting point. Making the jump from linear regression to logistic regression introduces many issues, including model fitting (least squares versus maximum likelihood), interpretability (odds ratios), interpretation of marginal effects (which are not constant for logistic regression), and regression diagnostics. These topics are covered fully in the context of logistic regression, and where appropriate, the comparison between linear and logistic regression is made. A chapter on probit analysis is also provided.


Table of contents

Series Editor’s Introduction
Preface
1 The Logic of Logistic Regression
Regression with a Dummy Dependent Variable
Transforming Probabilities into Logits
Linearizing the Nonlinear
Summary
2 Interpreting Logistic Regression Coefficients
Logged Odds
Odds
Probabilities
Tests of Significance
Standardized Coefficients
An Example
Summary
3 Estimation and Model Fit
Maximum Likelihood Estimation
Log Likelihood Function
Estimation
Tests of Significance Using Log Likelihood Values
Model Evaluation
An Example
Summary
4 Probit Analysis
Another Way to Linearize the Nonlinear
Probit Analysis
Interpreting the Coefficients
Maximum Likelihood Estimation
An Example
Summary
5 Conclusion
Notes
Appendix: Logarithms
The Logic of Logarithms
Properties of Logarithms
Natural Logarithms
Summary
References
About the Author
Acknowledgments
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