(1) Cov(B1, yı) (2) Cov(Bo, Y1) (3) Cov(Bo, j)

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Consider a linear regressin model, for i = 1, ..., n,
Yi = Bo + B1x;+ €;,
E(€;) = 0, V(€;) = o², and Cov(€i, €;) = 0 for i+ j.
Note that S = E ("; – 1)², Syy = E (4; – 9)², Say = E- ("; – #)(y; – 9).
Transcribed Image Text:Consider a linear regressin model, for i = 1, ..., n, Yi = Bo + B1x;+ €;, E(€;) = 0, V(€;) = o², and Cov(€i, €;) = 0 for i+ j. Note that S = E ("; – 1)², Syy = E (4; – 9)², Say = E- ("; – #)(y; – 9).
Find
(1) Cov(B1, Y1)
(2) Cov(Bo, Y1)
(3) Cov(Bo, 9)
Transcribed Image Text:Find (1) Cov(B1, Y1) (2) Cov(Bo, Y1) (3) Cov(Bo, 9)
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