The mean-squared error can be decomposed into the sum of (*) bias and this quantity.
The Gauss-Markov theorem states that OLS estimators have the lowest value of this quantity among the class of linear unbiased estimators. Efficient estimators are those for which this quantity has been minimized. Chebyshev's inequality states the probability of a random variable being more than a units away from the expected value is less than or equal to this quantity divided by a squared. This quantity is constant among all errors if the errors are homo•sked•astic. The mean-squared error can be decomposed into the sum of (*) bias and this quantity. It is identical to the second moment of a distribution when the first moment equals zero. In the definition of the normal distribution, the exponential contains the reciprocal of this quantity, whose estimated value is often represented by a lowercase "s" squared. For 10 points, name this quantity, the square of the standard deviation.
The Gauss-Markov theorem states that OLS estimators have the lowest value of this quantity among the class of linear unbiased estimators.
variance [or standard deviation before “a squared”] &lt;Bailey&gt;