Part 1 (33 Points) Consider the linear regression model: Y; = Bo + B1X1i + ui (1) 1. (4 pts) To determine if X1 is uncorrelated with the error term, ui, after estimating the model with OLS, you can see whether (1); X;=0. Do you agree or disagree? Explain. 2. (8 pts) Assume the errors in (1) are heteroskedastic. (a) Explain intuitively or with a figure what that means. (b) You use OLS to estimate B1. If your model satisfies all OLS assumptions except ho- moskedasticity will your estimate of B, be unbiased? 3. (6 pts) To make sure the residuals in (1 are heteroskedastic you decide to perform a test. You calculate the residuals from regression [1], ; and run the model: i = 70 + 71X1i + ei (2) You find that is not significantly different from zero. Given these results doy reject or fail to reject homoskedasticity? Why? Part 1 (33 Points) Consider the linear regression model: Y; = Bo + B1X1i + ui (1) 1. (4 pts) To determine if X1 is uncorrelated with the error term, ui, after estimating the model with OLS, you can see whether (1); X;=0. Do you agree or disagree? Explain. 2. (8 pts) Assume the errors in (1) are heteroskedastic. (a) Explain intuitively or with a figure what that means. (b) You use OLS to estimate B1. If your model satisfies all OLS assumptions except ho- moskedasticity will your estimate of B, be unbiased? 3. (6 pts) To make sure the residuals in (1 are heteroskedastic you decide to perform a test. You calculate the residuals from regression [1], ; and run the model: i = 70 + 71X1i + ei (2) You find that is not significantly different from zero. Given these results doy reject or fail to reject homoskedasticity? Why