When X and Y have expected values µX = µY = 0, Theorem 9.4 says that 
L (Y) = 
Show that this result is a special case of Theorem 9.7(a) when random vector Y is the 1- dimensional random variable Y.
Theorem 9.4
Random variables X and Y have expected values µX and µY, standard deviations σX and σY, and correlation coefficient ρ X,Y, The optimal linear mean square error (LMSE) estimator of X given Y is L (Y ) = a∗Y + b∗ and it has the following properties
Theorem 9.7(a)
