Gauss-markov theorem

This theorem states that when estimating parameters in a linear model (viz. the parameters appear linearly in the model), the linear least squares estimator is the most efficient (viz. with minimum variance, Estimator) of all unbiased estimators which can be reduced to linear functions of the data. There are cases where other estimators are more efficient, but they are not linear functions of the data.