Chapter 9. Regression Diagnostics Study Notes contains 16 pages covering the following learning objectives:

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

* Describe approaches to using heteroskedastic data.

* Characterize multicollinearity and its consequences; distinguish between multicollinearity and perfect collinearity.

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

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

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

* Describe methods for identifying outliers and their impact.

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

After reviewing these notes you will be able to apply what you learned with practice questions.

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