QA 9. Regression Diagnostics

Learning Objectives

1) Explain how to test whether a regression is affected by heteroskedasticity.

2) Describe approaches to using heteroskedastic data.

3) Characterize multicollinearity and its consequences; distinguish between multicollinearity and perfect collinearity.

4) Describe the consequences of excluding a relevant explanatory variable from a model and contrast those with the consequences of including an irrelevant regressor.

5) Explain two model selection procedures and how these relate to the bias-variance trade-off.

6) Describe the various methods of visualizing residuals and their relative strengths.

7) Describe methods for identifying outliers and their impact.

8) Determine the conditions under which OLS is the best linear unbiased estimator.