Regression with a Polytomous Dependent Variable Regression with a Polytomous Dependent Variable
Regression with a Polytomous Dependent Variable Regression with a Polytomous Dependent Variable
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
Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.
Please, subscribe or login to access full text content.
If you think you should have access to this title, please contact your librarian.
To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .
