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Multiple Regression with Discrete Dependent Variables$
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John G. Orme and Terri Combs-Orme

Print publication date: 2009

Print ISBN-13: 9780195329452

Published to Oxford Scholarship Online: May 2009

DOI: 10.1093/acprof:oso/9780195329452.001.0001

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Regression with a Dichotomous Dependent Variable Regression with a Dichotomous Dependent Variable

Regression with a Dichotomous Dependent Variable Regression with a Dichotomous Dependent Variable

Chapter:
(p.30) 2 Regression with a Dichotomous Dependent Variable
Source:
Multiple Regression with Discrete Dependent Variables
Author(s):

John G. Orme

Terri Combs-Orme

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195329452.003.0002

This chapter describes the use of binary logistic regression (also known simply as logistic or logit regression), a versatile and popular method for modeling relationships between a dichotomous dependent variable and multiple independent variables. In logistic regression, the estimated value, L, is the natural logarithm (or simply log) of the odds, typically called the logit. Probabilities, odds, logits, and odds ratios (OR) are defined and illustrated, and the link function is explained. The chapter also discusses centering, confidence intervals, nested models, and outliers.

Keywords:   probabilities, odds, logits, odds ratios, centering, confidence intervals, nested models, outliers, link function

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