Let (x1,Y1), (x2,Y2),...,(xn,Yn) satisfy
               Yi = beta_0 + beta_1*xi+ei , i = 1,...,n,
where e1,e2,...,en ~ N(0,sigma^2). Show that S^2 is unbiased estimator
of sigma^2, where
               S^2 = 1/(n-2)Sum(Yi-beta^_0-beta^_1*xi)^2
and beta^_0 and beta^_1 are the least-square estimators of beta_0 and
beta_1, respectively.