Detailed steps/answers would be greatly appreciated so I understand how you arri

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Detailed steps/answers would be greatly appreciated so I understand how you arrived at the answers. Thank you!

We consider a simple regression model given by y_i = beta x_i + u_i for i = 1,2, n, where we assume x_i is non-random. Note that we do not have the constant term alpha. Though the error term u_i is independent over i, we assume that E(u_i) = 0 and E(u_i ^2) = sigma _i ^2 of for each i. In order to handle heteroskedasticity, we estimate beta using the WLS (weighted least squares). Assuming that we observe sigma_i ^2 for all i, obtain the WLS estimator of beta, which we denote beta. Find E(beta) and var(beta). We now assume that sigma_i^2 = cx_i ^2 for some constant c > 0. In this case, compare var(beta) in part (d) with var(beta) in part (e). Which one is larger? Docs your answer justify the use of WLS in this case?

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Detailed steps/answers would be greatly appreciated so I understand how you arri

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