Logistic Regression : A Primer

by Fred C. Pampel

• ISBN13: 9780761920106
• ISBN10: 0761920102
• Edition: 1st
• Format: Paperback
• Copyright: 2000-05-26
• Publisher: Sage Publications, Inc

Summary

Trying to determine when to use a logistic regression and how to interpret the coefficients? Frustrated by the technical writing in other books on the topic? Pampela??s book offers readers the first "nuts and bolts" approach to doing logistic regression through the use of careful explanations and worked out examples. Pampel first offers readers a review of some basic concepts, such as exponents, percentage change, and likelihood functions. Next, he describes in some detail how taking the log of the odds eliminates the floor so that the transformation of logistic regression coefficients into coefficients that effect odds and probabilities makes more sense to readers. And, third, he describes maximum likelihood estimation through words and simple samples (along side of the formulas) so as to make the concept more concrete and the procedure easier to comprehend. Throughout the book, he emphasizes examples, explanations, and how to interpret the results of each procedure. This book will enable readers to use and understand logistic regression techniques and will serve as a foundation for more advanced treatments of the topic.

Table of Contents

Series Editor's Introduction	v
Preface	vii
The Logic of Logistic Regression	1	(17)
Regression with a Dummy Dependent Variable	1	(9)
Transforming Probabilities into Logits	10	(4)
Linearizing the Nonlinear	14	(4)
Summary	18	(1)
Interpreting Logistic Regression Coefficients	18	(21)
Logged Odds	19	(2)
Odds	21	(2)
Probabilities	23	(7)
Tests of Significance	30	(2)
Standardized Coefficients	32	(3)
An Example	35	(4)
Summary	39	(1)
Estimation and Model Fit	39	(15)
Maximum Likelihood Estimation	40	(3)
Log Likelihood Function	43	(1)
Estimation	44	(1)
Tests of Significance Using Log Likelihood Values	45	(3)
Model Evaluation	48	(3)
An Example	51	(2)
Summary	53	(1)
Probit Analysis	54	(14)
Another Way to Linearize the Nonlinear	54	(4)
Probit Analysis	58	(2)
Interpreting the Coefficients	60	(4)
Maximum Likelihood Estimation	64	(1)
An Example	65	(3)
Summary	68	(1)
Conclusion	68	(3)
Notes	71	(3)
Appendix: Logarithms	74	(9)
The Logic of Logarithms	74	(2)
Properties of Logarithms	76	(3)
Natural Logarithms	79	(3)
Summary	82	(1)
References	83	(2)
About the Author	85	(1)
Acknowledgments	86