Weighted least squares is a statistical method used to estimate the parameters of a linear regression model when the variance of the errors varies across observations. By applying different weights to different data points, this technique accounts for heteroscedasticity, which improves the accuracy and efficiency of the parameter estimates. This approach contrasts with ordinary least squares, where all data points are treated equally, potentially leading to biased results when the assumption of constant variance is violated.
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