Applied Logistic Regression Analysis

by Scott William Menard (Editor)

"Here is another, number 106, of the concise, practical, and economical little paperbacks in the publisher′′s Quantitative Applications in the Social Sciences Series. . . . this is one of the best. . . . the author systematically presents each aspect of MLR practice, then introduces the corresponding aspect of logistic regression analysis (LRA) by the method of comparison and contrast. It is a very effective style. . . . The book is well written and interesting to read in its entirety. Concepts are clearly presented, logically motivated, and effectively illustrated. . . . Any statistician would be proud to have written this book."
--Technometrics

Aimed at the student or professional whose statistical background may not extend beyond ordinary least squares multiple regression analysis, Brown introduces logistic regression as an extension of linear regression and emphasizes the parallels between the two. Unique features of the book include detailed consideration of goodness of fit and indices of predictive efficiency, standardized logistic regression coefficients, and the use of existing SPSS and SAS software for loglinear and logit models to calculate logistic regression models for polytomous nominal and ordinal dependent variables. With an emphasis on application of computer statistical packages rather than theorems and proofs, Brown offers a nontechnical and applied introduction to this useful topic.

104 pages, Paperback

First published June 29, 1995

Reviews

[Nmoversae Company · Rating 5 out of 5]

I enjoyed reading this book
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[J. Scott Frampton · Rating 3 out of 5]

This is a good book. It provides a clear understanding of logistic regression analysis. I use it as a textbook for a number of my statistics courses.

[Steven Peterson · Rating 4 out of 5]

Logistic regression analysis is an analogue to multiple regression--with the dependent variable pitched at the dichotomous level (just two values). It is a pretty sturdy statistical technique, not demanding a lot of assumptions about the nature of dependent and independent variables. This book is not terribly accessible (I get headaches in some sections), but it is a very nice brief introduction to the subject.

[Walker*Management*Library · Rating 5 out of 5]

Recommended by Dawn Iacobucci for wannabe geeks.