Weighted least squares is a statistical technique used to estimate the parameters of a linear model by minimizing the sum of the squared differences between observed and predicted values, with each difference being multiplied by a weight. This method is particularly useful when dealing with heteroscedasticity, where the variability of the errors differs across observations, allowing for more accurate estimations by giving different levels of importance to each data point based on its reliability.
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