Summary
Contents
Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the substantive meaning of each assumption, for example, lack of measurement error, absence of specification error, linearity, homoscedasticity and lack of autocorrelation.
Introduction
Introduction
Numerous assumptions are made—implicitly, if not explicitly—whenever regression analysis is used in social science research.1 Quantitative social science has become prevalent enough that nearly all second-year graduate students can recite a list of the standard regression assumptions. Yet these assumptions are often learned by rote, ...
