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7. For the model Y₁ = ẞo + B₁xi + Єi, i = 1, . . ., n (a). Prove E(Bo) = ßo. (b). Prove Var(30) = 2Σ n (1/1)² = 0² ( 1 + Σ (21-2)²). (1/12 (−x)2 (c). Prove the covariance between Bo and B₁ is Cov(B0, B1) σ²x Σ(;-π)2

7. For the model Y₁ = ẞo + B₁xi + Єi, i = 1, . . ., n (a). Prove E(Bo) = ßo. (b). Prove Var(30) = 2Σ n (1/1)² = 0² ( 1 + Σ (21-2)²). (1/12 (−x)2 (c). Prove the covariance between Bo and B₁ is Cov(B0, B1) σ²x Σ(;-π)2

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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7. For the model Y₁ = ẞo + B₁xi + Єi, i = 1, . . ., n (a). Prove E(Bo) = ßo. (b). Prove Var(30) = 2Σ n (1/1)² = 0² ( 1 + Σ (21-2)²). (1/12 (−x)2 (c). Prove the covariance between Bo and B₁ is Cov(B0, B1) σ²x Σ(;-π)2
Transcribed Image Text:7. For the model Y₁ = ẞo + B₁xi + Єi, i = 1, . . ., n (a). Prove E(Bo) = ßo. (b). Prove Var(30) = 2Σ n (1/1)² = 0² ( 1 + Σ (21-2)²). (1/12 (−x)2 (c). Prove the covariance between Bo and B₁ is Cov(B0, B1) σ²x Σ(;-π)2
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