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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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Introduction to Regression Modeling Introduction to Regression Modeling

Introduction to Regression Modeling Introduction to Regression Modeling

(p.3) 1 Introduction to Regression Modeling
Multiple Regression with Discrete Dependent Variables

John G. Orme

Terri Combs-Orme

Oxford University Press

This chapter is a brief review of some major concepts of linear regression, presented in the context of simple examples using both dichotomous and continuous independent variables. The chapter compares and contrasts linear regression and the regression models for discrete dependent variables discussed in the remaining chapters of the book in order to clarify the major concepts. This chapter explains the generalized linear model (GZLM) in the context of linear regression and discuss and illustrates residuals, spurious relationships, interactions and curvilinear relationships, and multicollinearity. In preparation for the regression models discussed in subsequent chapters, the chapter also explains the link function, maximum likelihood estimation, issues related to sample size, assumptions and limitations, and model specification and evaluation.

Keywords:   generalized linear model, residuals, spurious relationships, interactions, curvilinear relationships, multicollinearity, link function, maximum likelihood estimation, sample size

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