The Gauss-Markov Theorem states that, in a linear regression model where the errors have an expected value of zero and are uncorrelated, the ordinary least squares (OLS) estimator is the best linear unbiased estimator (BLUE) of the parameters. This theorem is fundamental in statistical inference because it guarantees that OLS estimators have minimum variance among all linear unbiased estimators, making them efficient under certain conditions.
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