In Example 12.4.1, in contrast to Example 10.2.1, when we introduced the contaminated distribution for εi, we did not introduce a bias. Show that if we had, it would not have mattered. That is, if we assume

then:
(a) The least squares estimator b would still be an unbiased estimator of β.
(b) The least squares estimator a has expectation α + δμ, so the model may just as well be assumed to be Yi = α + δμ + βxi + εi.

(Eei , Var )-, (0, *) . (AT2) with probability 1-5 with probability ,