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 Formal Presentation of the Regression Assumptions
The Regression Surface

In the standard multivariate regression model, a dependent variable, Y, is assumed to be a function of a set of k independent variables (or regressors), X1, X2, …, Xk, in some population. The model assumes that for each set of values ...

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