Hello, I'm currently stuck at (d). I have derived to this point:
![Var_{x}[(g^{-}(x) - y)^{2}] = E[((g^{-}(x) - y)^{2} - E[(g^{-}(x) - y)^{2}])^{2}]](/vblatex/img/db726e1946931050cda97a35e3295ec0-1.gif "Var_{x}[(g^{-}(x) - y)^{2}] = E[((g^{-}(x) - y)^{2} - E[(g^{-}(x) - y)^{2}])^{2}]")
![E[((g^{-}(x) - y)^{2} - E[(g^{-}(x) - y)^{2}])^{2}] = E[(g^{-}(x) - y)^{4}] - (E[(g^{-}(x) - y)^{2}])^{2}](/vblatex/img/99aae34ae881029c0ae8883d316ca61b-1.gif "E[((g^{-}(x) - y)^{2} - E[(g^{-}(x) - y)^{2}])^{2}] = E[(g^{-}(x) - y)^{4}] - (E[(g^{-}(x) - y)^{2}])^{2}")
and I'm currently stuck. The hint says that the squared error is unbounded hence I guess that there should be no bound for expected value of squared error?
(I'm not good at math though...)