### Lecture: Heteroskedasticity, Weighted Least Squares, and Variance Estimation (Advanced Data Analysis from an Elementary Point of View)

Weighted least squares estimates, to give more emphasis to particular data
points. Heteroskedasticity and the problems it causes for inference. How
weighted least squares gets around the problems of heteroskedasticity, if we
know the variance function. Estimating the variance function from regression
residuals. An iterative method for estimating the regression function and the
variance function together. Locally constant and locally linear
modeling. Lowess.

*Reading*: Notes, chapter 7

*Optional reading*: Faraway, section 11.3.

Advanced Data Analysis from an Elementary Point of View

Posted at February 07, 2013 10:30 | permanent link