Weighted Least Squares (WLS) is a statistical technique that is used to estimate the parameters of a linear regression model. In a standard linear regression model, all data points are given equal weight, which can lead to inaccurate estimates if there are outliers or other data points that are more important than others. WLS addresses this issue by giving different weights to different data points, depending on their importance.

The main idea behind WLS is to minimize the sum of the squared differences between the predicted values and the actual values, while taking into account the relative importance of each data point. This is achieved by assigning weights to each data point based on its variance or standard deviation. The higher the variance of a data point, the lower its weight, and vice versa.

WLS is useful in situations where there is heteroscedasticity, which means that the variance of the errors is not constant across the range of data. This can happen, for example, when the data points are spread out over a wide range or when there are outliers that have a large impact on the regression model. WLS can help to correct for this by giving more weight to data points that have a lower variance and less weight to data points that have a higher variance.

In summary, WLS is a powerful statistical technique that can help to improve the accuracy of linear regression models by taking into account the relative importance of each data point. By assigning weights to each data point based on its variance, WLS can help to correct for heteroscedasticity and produce more robust estimates of the parameters of a linear regression model.