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~ 5. Assume that the assumptions (9.9) for the linear model are valid. During the lectures we derive for the ML estimator of the slope the following result b(Y) = B(Y) · N(ẞ,0²/qxx). In order to calculate the variance, we need the following probability calculation result which is the content of this task. Assume that Y₁,..., Yn ~ N(0,σ²) H. Show by calculating that 2 E(YY)(YY) = n holds for each i, j = 1,...,n. (Hint: if EX the lecture notes is also useful). = + Jo² kun i = j kun i j 0, then E(X2) = varX. Page 35 of

~ 5. Assume that the assumptions (9.9) for the linear model are valid. During the lectures we derive for the ML estimator of the slope the following result b(Y) = B(Y) · N(ẞ,0²/qxx). In order to calculate the variance, we need the following probability calculation result which is the content of this task. Assume that Y₁,..., Yn ~ N(0,σ²) H. Show by calculating that 2 E(YY)(YY) = n holds for each i, j = 1,...,n. (Hint: if EX the lecture notes is also useful). = + Jo² kun i = j kun i j 0, then E(X2) = varX. Page 35 of

MATLAB: An Introduction with Applications
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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~ 5. Assume that the assumptions (9.9) for the linear model are valid. During the lectures we derive for the ML estimator of the slope the following result b(Y) = B(Y) · N(ẞ,0²/qxx). In order to calculate the variance, we need the following probability calculation result which is the content of this task. Assume that Y₁,..., Yn ~ N(0,σ²) H. Show by calculating that 2 E(YY)(YY) = n holds for each i, j = 1,...,n. (Hint: if EX the lecture notes is also useful). = + Jo² kun i = j kun i j 0, then E(X2) = varX. Page 35 of
Transcribed Image Text:~ 5. Assume that the assumptions (9.9) for the linear model are valid. During the lectures we derive for the ML estimator of the slope the following result b(Y) = B(Y) · N(ẞ,0²/qxx). In order to calculate the variance, we need the following probability calculation result which is the content of this task. Assume that Y₁,..., Yn ~ N(0,σ²) H. Show by calculating that 2 E(YY)(YY) = n holds for each i, j = 1,...,n. (Hint: if EX the lecture notes is also useful). = + Jo² kun i = j kun i j 0, then E(X2) = varX. Page 35 of
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