The difficulty, in practice, is determining estimates of the error variances (or standard deviations).Some key points regarding weighted least squares are: We consider some examples of this approach in the next section. \(\begin^2\).Īfter using one of these methods to estimate the weights, \(w_i\), we then use these weights in estimating a weighted least squares regression model. The method of weighted least squares can be used when the ordinary least squares assumption of constant variance in the errors is violated (which is called heteroscedasticity). The method of ordinary least squares assumes that there is constant variance in the errors (which is called homoscedasticity).
