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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 Count Dependent Variable Regression with a Count Dependent Variable

Regression with a Count Dependent Variable Regression with a Count Dependent Variable

Chapter:
(p.152) 5 Regression with a Count 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/9780195329452.003.0005

Count variables indicate the number of times a particular event occurs to each case, usually within some domain of observation such as a given time period, population size, or geographical area. This chapter first describes Poisson regression, which is the basic model upon which many other regression models for counts are based, and which uses the log as the link function This chapter also discusses negative binomial regression, an alternative to Poisson regression with less restrictive assumptions. The chapter discusses offset variables and the importance of accounting for exposure when using these models. Equidispersion, an assumption of the Poisson model, and multicollinearity are also discussed and illustrated.

Keywords:   count variables, Poisson regression, negative binomial regression, offset variables, equidispersion, multicolinearity

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