Regression Diagnostics (FRM Part 1 2023 - Book 2 - Chapter 9)

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After completing this reading, you should be able to:
- Explain how to test whether 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 tradeoff.
- 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.

[@basselmtaweh3103]

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[@omkarchalke6297]

so the outlier is (-j)