ABSTRACT
This chapter explains ordinary least-squares estimators and estimates have some very desirable statistical qualities. These qualities are captured in the acronym BLUE- best, linear, unbiased estimators. The unbiased results are desirable in any scientific study. This is true of regression analysis as well. However, the term unbiased has a particular meaning in regard to statistical estimators. An estimator is unbiased if its expected value is equal to the true value of the parameter. The chapter also discusses the Monte Carlo study an econometric study that draws repeated samples from a known population and then analyzes the characteristics of the sample regressions. It explains that heteroskedasticity error terms of a regression do not all have the same variance. The chapter also describes that the ordinary least-squares estimators are best.
