Consider a linear regressin model, for i = 1, ..., n, * N(0, o*). iid Yi = Bo + B1; + €;, where Note that (1) S = E("; - 1)°, Syy = E-(4, - 9, Szy = E1("; - T)(y; – 9). (2) SST = E1(y - 9), SSR = E ( - , SSE = E(yi – )². 1. One of the good estimators for o' in a simple regression model is n – 2 i=1 (1) Show s² is an unbiased estimator for o?. That is, show E(s²) = o². (2) E(MSE) = o², where MSE SSE n-2

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Consider a linear regressin model, for i = 1, .. . , n,
N(0, o?).
iid
Yi = Bo + B10, + €;, where €;
Note that
(1) S = E(*,– 1), Syy = (u, – 9). Szy = -1("; - 1)(y; – T).
(2) SST = "( - y), SSR = E(9 – 9)², SSE =
|
1. One of the good estimators for o' in a simple regression model is
(1) Show s² is an unbiased estimator for o². That is, show E(s?) = o².
(2) E(MSE) = o, where MSE
SSE
n-2
Transcribed Image Text:Consider a linear regressin model, for i = 1, .. . , n, N(0, o?). iid Yi = Bo + B10, + €;, where €; Note that (1) S = E(*,– 1), Syy = (u, – 9). Szy = -1("; - 1)(y; – T). (2) SST = "( - y), SSR = E(9 – 9)², SSE = | 1. One of the good estimators for o' in a simple regression model is (1) Show s² is an unbiased estimator for o². That is, show E(s?) = o². (2) E(MSE) = o, where MSE SSE n-2
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