Summary

In this section, we learned why OLS assumptions are important, how to diagnose assumption violations in OLS regression, and how to conduct sensitivity analysis and correct for some assumption violations.

The issues covered include linearity and model specification, perfect and high multicollinearity, constant error variance, independence of error term observations, influential observations, and normality test.