Let Bo and ₁ be least square estimators for the simple linear regression model y = Bo+B₁x+e.Prove that E(ŝo) = ßo. Finish the proof based on the following hints.Hint 1: Note that Bo = ÿ – Â₁ñ.-E (B³) = € ( Ñ - B‹ ñ) = 6 (Y) – E(B₁A)-Hint 2: first calculate E(). Note that y = (y₁ + y₂ + ...y2 +yn)/n. In the class we alreadycalculated what is E(y₁), E(y2),..., E(yn), based on these existing results, figure out what isE(ÿ).Yıt2€ (g) = € ( Y₁+ y₂ + ² X ²) = √( @cy₁+ y₂ + ... ₁₂)есунnYn)., n as fixed (not random..."Hint 3: next we calculate E(₁). Note that if we treat x1, x2,variable), then E(3₁x) = x · E(B₁).

Let ßo and ₁ be least square estimators for the simple linear regression model y = ßo+B₁x+ɛ. Prove that E(Bo) = 3o. Finish the proof based on the following hints. Hint 1: Note that Bo = y - B₁. E (B) = 6 (ỹ - B₁x) = 6 (Y) - E(B₁7) Hint 2: first calculate E(). Note that y = (y₁ + y2 +...+yn)/n. In the class we already calculated what is E(y₁), E(y₂),..., E(yn), based on these existing results, figure out what is E(ÿ). N € (y) = € ( = Y₁+ y₂ + y^²) ==√ √ E (Y₁+ y₂ + ... Yn) n Hint 3: next we calculate E(Â₁7). Note that if we treat x₁,x2,...,n as fixed (not random variable), then E(₁x) = ñ · E(ŝ₁).

Let ßo and ₁ be least square estimators for the simple linear regression model y = ßo+B₁x+ɛ. Prove that E(Bo) = 3o. Finish the proof based on the following hints. Hint 1: Note that Bo = y - B₁. E (B) = 6 (ỹ - B₁x) = 6 (Y) - E(B₁7) Hint 2: first calculate E(). Note that y = (y₁ + y2 +...+yn)/n. In the class we already calculated what is E(y₁), E(y₂),..., E(yn), based on these existing results, figure out what is E(ÿ). N € (y) = € ( = Y₁+ y₂ + y^²) ==√ √ E (Y₁+ y₂ + ... Yn) n Hint 3: next we calculate E(Â₁7). Note that if we treat x₁,x2,...,n as fixed (not random variable), then E(₁x) = ñ · E(ŝ₁).

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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