1)(20 points) The last lessons have spent a lot of time describing the slope and intercept terms (and their variances) of the one-variable sample regression function. We also know that for any particular value of the independent variable (call it X0), that the predicted value of Y0 is. (This is sometimes called a “point prediction.”)

a)(10) Prove thatis an unbiased estimator of E[Y0|X0].

b)(10) Derive the formula for the variance of. Show at least two steps in this derivation.

a.Hint 1: You are looking for. This is the variance of a sum of two random variables. What is the general formula for such a sum? (Go back to week 2 lectures, if you need a reminder.) Use that formula now.

b.Hint 2: If you did hint 1 correctly, you will see you need the formula for. Take it on faith that this can be found to be. (You might find it interesting that the two estimators have a negative correlation. A steeper slope tends to imply a lower intercept, and vice versa.)

<pclass=”p15″>1)(20 points) Compare the following two regressions:i.i.Equation i. is exactly the regression we’ve been working with thus far, so all the formulas we’ve derived thus far apply. In equation ii the independent variable has been multiplied by 2. How does this change, if at all, the values of,,, R2, and SSE?

<pclass=”p0″>4.(20 points) Repeat your regression from problem 1. This time, do it in STATA.a)(5) Report the values of the slope, intercept, and the R2. Confirm the results are identical to what you did in Excel.

b)(5) Reverse the regression. That is, switch the X and Y variables. Report the same results.

c)(5) Has the value of R2 changed? Explain why or why not.

d)(5) For the version of the regression where the X variable is Corruption, calculate the p-value for a two-tailed test where the null hypothesis is ?1= -3600.