Applied Logistic Regression / Edition 1

Applied Logistic Regression / Edition 1

by David W. Hosmer Jr., Stanley Lemeshow
     
 

ISBN-10: 0471615536

ISBN-13: 9780471615538

Pub. Date: 01/28/1989

Publisher: Wiley

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

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Overview

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.

Product Details

ISBN-13:
9780471615538
Publisher:
Wiley
Publication date:
01/28/1989
Series:
Wiley Series in Probability and Statistics - Applied Probability and Statistics Section Series, #237
Edition description:
Older Edition
Pages:
328
Product dimensions:
6.33(w) x 9.31(h) x 0.99(d)

Related Subjects

Table of Contents

Introduction to the Logistic Regression Model.
The Multiple Logistic Regression Model.
Interpretation of the Coefficients of the Logistic Regression Model.
Model-Building Strategies and Methods for Logistic Regression.
Assessing the Fit of the Model.
Application of Logistic Regression with Different Sampling Models.
Logistic Regression for Matched Case-Control Studies.
Special Topics.
References.
Index.

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