8Some Common Problems in Regression Analysis
In Chapter 3, we discussed a series of assumptions, called the classical linear regression model (CLRM) assumptions. Later, in Chapter 5, we added the assumption of “no perfect multicollinearity” to that list. It was noted that if the CLRM assumptions are met, then there is a famous theorem, called the Gauss-Markov theorem, that shows that OLS is the best, linear, unbiased estimator (i.e., OLS is “BLUE”). This is a very powerful result as it provides a strong justification for the use of OLS over other linear, unbiased estimators we could consider.
It is often the case, however, that one or more of the CLRM assumptions are not satisfied. If this happens, then OLS is ...
Get Regression Basics, 3rd Edition now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
