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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 Polytomous Dependent Variable Regression with a Polytomous Dependent Variable

Regression with a Polytomous Dependent Variable Regression with a Polytomous Dependent Variable

Chapter:
(p.91) 3 Regression with a Polytomous 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/9780195329452.003.0003

This chapter describes the use of multinomial logistic regression (also known as polytomous or nominal logistic or logit regression or the discrete choice model), a method for modeling relationships between a polytomous dependent variable and multiple independent variables. Polytomous variables have three or more unordered categories and are often called multicategorical or multinomial (the assumed underlying distribution). The chapter also discusses the testing and presentation of interactions and curvilinear relationships with multinomial logistic regression, as well as the assumptions of the model.

Keywords:   multinomial logistic regression, polytomous dependent variables, nominal logistic regression, logit regression, discrete choice model, multicategorical variables, multinomial variables, interactions, curvilinear relationships, assumptions

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