I'm a little stuck on this review problem so help would be greatly appreciated!
Q: We have the regression model $Y_i=\beta_0+\beta_1X_i+\mu_i$ and we assume that the expected errors are $0$. We also know that the asymptotic variance of $\hat{\beta_1}$ is: $$\frac{Var(\mu_i(X_i-E(X_i))}{Var(X_i)}$$
EDITED: Now I have to Show that IF $E[\mu_i^2|X_i]=\sigma^2$ THEN $Var(\mu_i|X_i)=\sigma^2$.
My thoughts: What does knowing the asymptotic variance have to do with anything in this problem? Can I simply solve the following equation?
$$Var(\mu_i^2|X_i)=E[\mu_i^2|X_i] - E([\mu_i|X_i])^2$$
But how?
Step by step help would be greatly appreciated. Many thanks in advance!