See questions below on "disturbances"" -in the regression model: y = Xb + e where yhat = Xb The disturbances, or errors, represent the difference between Xb (yhat) and y. In the real world, we do not know the true errors or disturbances. We simply have estimates of them, in the form of the regression residuals. (This is because we have a sample of y values from some distribution rather than a set of fixed y's.) One of our OLS regression assumptions is that the disturbances follow a normal distribution that is centered at zero. This means that the E[error] = 0. To evaluate whether or not this assumption holds, we look at our residuals. These are not exactly the same as the true disturbances, but the best material we have to work with. This is why we are comparing the distribution of the residuals with a t distribution rather than a normal--to reflect this added uncertainty. I looked at the distribution of the y values in the handout example after looking at the distribution of the residuals in the qq.plot because I wanted to see if a skew in the distribution of the y values might be partially responsible for the skew in the residual distribution. You can see how the two are linked directly from the equation above. On White's estimator, see p. 305 in Fox. It is calculate different, robust standard errors for the coefficients using the residuals from the regression. White's method does not calculate new regression coefficients. The last homework assignment will be due the last day of lecture. Good luck! Alison