Weighted Least Squares is a regression method that assigns different weights to data points based on the variance of their errors. In contrast to OLS, which assumes constant variance across all observations, WLS acknowledges that some data points may have more variability than others and adjusts the fitting process accordingly. This adjustment provides more reliable estimates, particularly when dealing with datasets that exhibit non-constant variance, which is a common scenario in dose-response studies.
