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.

A “Weighty” Illustration

To illustrate the “substantive meanings” of the regression assumptions, throughout this monograph I will explore a regression model explaining human weight in a population of 134 women, aged 34 to 59. And I will assume that we are privileged—as we are not in the real world of research—to know the ...

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