Lesson 13: Weighted Least Squares & Robust Regression

Overview Section

So far we have utilized ordinary least squares for estimating the regression line. However, aspects of the data (such as nonconstant variance or outliers) may require a different method for estimating the regression line. This lesson provides an introduction to some of the other available methods for estimating regression lines. To help with the discussions in this lesson, recall that the ordinary least squares estimate is

\(\begin{align*} \hat{\beta}_{\textrm{OLS}}&=\arg\min_{\beta}\sum_{i=1}^{n}\epsilon_{i}^{2} \\ &=(\textbf{X}^{\textrm{T}}\textbf{X})^{-1}\textbf{X}^{\textrm{T}}\textbf{Y} \end{align*}\)

Because of the alternative estimates to be introduced, the ordinary least squares estimate is written here as \(\hat{\beta}_{\textrm{OLS}}\) instead of b.


Upon completion of this lesson, you should be able to:

  • Explain the idea behind weighted least squares.
  • Apply weighted least squares to regression examples with nonconstant variance.
  • Understand the purpose behind robust regression methods.

 Lesson 13 Code Files

Below is a zip file that contains all the data sets used in this lesson:


  • ca_learning.txt
  • home_price.txt
  • market_share.txt
  • quality_measure.txt