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.

The Substantive Meaning of Regression Assumptions
Drawing Dynamic Inferences From Cross-Sectional Regressions

As noted in Chapter 2, regression models can be cross-sectional (where the cases for analysis are multiple units observed at a single point in time) or time-series (where the cases are observations of a single unit at multiple points in ...

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