Applied Logistic Regression

by David W. Hosmer, Stanley Lemeshow

Shows how to model a binary outcome variable from a linear regression analysis point of view. Develops the logistic regression model and describes its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariates. Following establishment of the model there is discussion of its interpretation. Several data sets are the source of the examples and the exercises, and a number of software packages are used to analyze data sets, including BMDP, EGRET, GLIM, SAS, and SYSTAT.

Genres: Nonfiction, Research

328 pages, Hardcover

First published July 1, 1989

Reviews

[Steven Peterson]

Logistic regression is a very handy statistical tool. It does not demand much of the data (no need for variables to be normally distributed). Also, it can be used with dichotomous dependent variables. Multiple regression is often used for such purposes, but--technically--that might provide misleading results. This is one of the classic books outlining the use (and abuse) of logistic regression analysis.

[Henry Ratul]

This book is awesome to self-learn in-depth about logistic regression. The author designed the book nicely with step by step explanation of real-world data.

[Nmoversae Company]

I enjoyed reading this book
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