# 1 2(ans given our model y i = β β 1 x i u i e u 2 i

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Unformatted text preview: 1 2. (Ans) Given our model Y i = β + β 1 X i + u i , E ( u 2 i | X i ) = V ar ( u i | X i ) = V ar ( Y i | X i ). The first equality comes from V ar ( u i | X i ) = E ( u 2 i | X i )- E ( u i | X i ) 2 and the assumption E ( u i | X i ) = 0 while the second equality comes from the fact that given X i (i.e., if we know X i ), the variation of Y i only comes from the variation of u i . Therefore, we only need to find P ( Y i = 1 | X i ) and P ( Y i = 0 | X i ) to calculate E ( u 2 i | X i ). Let us take the conditional expectations on both sides of the model. From the model Y i = β + β 1 X i + u i , we get E ( Y i | X i ) = E ( β + β 1 X i + u i | X i ) = E ( β | X i ) + E ( β 1 X i | X i ) + E ( u i | X i ) = β + β 1 X i . Here we exploited the properties of conditional expectations (which should be familiar) and the assumption E ( u i | X i ) = 0. On the other hand, since Y i is a Bernoulli random variable given X i , E ( Y i | X i ) = 1 · P ( Y i = 1 | X i ) + 0 · P ( Y i = 0 | X i ) = P ( Y i = 1 | X i ). This combined with the above result yields, P ( Y i = 1 | X i ) = β + β 1 X i P ( Y i = 0 | X i ) = 1- P ( Y i = 1 | X i ) = 1- β- β 1 X i ....
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1 2(Ans Given our model Y i = β β 1 X i u i E u 2 i | X i...

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