Weighted least squares is a statistical method used to estimate the parameters of a regression model by minimizing the sum of the squared differences between observed and predicted values, while giving different weights to each observation. This technique is particularly useful when the observations have different levels of variance, allowing for a more accurate estimation by accounting for heteroscedasticity in the data.
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