One consequence of this weighting is that instances that have extreme values (outliers) can have a disproportionately large impact on the line-fitting process, resulting in the line being dragged away from the other instances. Thus, it is important to check for outliers in a data set prior to fitting a line to the data set (or, in other words, training a linear regression function on the data set) using the least squares algorithm.
