Dave, We dissected the formula for sampling variance of multiple regression (see Fox 217), which is the V(b) = sigma^2(X'X)^ -1 into its component parts, and solved to get a big messy-looking variance-covariance matrix. Then showed the elements on the diagonal were equal to V(A) and V(B), respectively. The following isn't as clear as I'd hoped, but here is a description of what we did. Using the form given on Fox 204-5, where X = row vector of regressors (with 1 as a constant) and Yi = (1, x1, x2.xik) So, in matrix form, you have a column vector of Ys = (matrix with 1s in the first column, xs in the other columns) So we can write X as a 2xn matrix, with 1s in the first column and the row of x's in the second. Therefore X' is a row vector with first row of 1s and second row of x's. Plugging these in, you can solve for X'X, (X'X)^1, etc. This gives the variance-covariance matrix. You'll can then work with the formulas on the diagonal and show that they are equal to the formulas V(A) (show it's equal to first element in var-covar matrix) and V(B) (show that it's equal to the bottom-right element in var-covar matrix). (see Alison's handout for the two equations you have to show are equal). Brodi -----Original Message----- From: gov1000-list-bounces(a)lists.fas.harvard.edu [mailto:gov1000-list-bounces@lists.fas.harvard.edu] On Behalf Of David Ni Sent: Sunday, November 28, 2004 2:52 PM To: gov1000-list(a)lists.fas.harvard.edu Subject: [gov1000-list] problem 3? I'm not sure if I'm the only one, but I'm not quite sure how to do problem 3. any tips? Thanks! Cheers, Dave