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.
The Consequences of the Regression Assumptions Being Satisfied
The Consequences of the Regression Assumptions Being Satisfied
If assumptions A1 through A7 (i.e., all but the assumption of a normally distributed error term) hold, the Gauss-Markov theorem ensures that OLS estimators for a regression model's coefficients have two desirable properties: they are unbiased and efficient (Berry & Feldman, ...
