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
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
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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