Probability and Statistics
Weighted least squares is a statistical method used for estimating the parameters of a linear regression model when the residuals have non-constant variance, also known as heteroscedasticity. This technique assigns different weights to each observation in the dataset, allowing for more reliable estimation by emphasizing certain data points over others based on their variance. By doing so, it provides a better fit for the model compared to ordinary least squares, especially when the assumptions of constant variance are violated.
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