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a 5. Given a bivariate data set {xi,yi}=1, we will form the best-ft linear regression model ŷ = a + bx by the Method of Least Squares. Recall that the total error is n n = - Σy - ji) = Σvi - a - bx;)2 i=1 i=1 Show that the coefficients for the best-fit line at the critical point are determined the following: α= (Σy)(Σ x?) - (Σπ;)(Συ;3;) ηΣ - (Συ;)2 nΣviyi - (Σα;)(Σyi) nΣx - (Σπ;)2 ότη

a 5. Given a bivariate data set {xi,yi}=1, we will form the best-ft linear regression model ŷ = a + bx by the Method of Least Squares. Recall that the total error is n n = - Σy - ji) = Σvi - a - bx;)2 i=1 i=1 Show that the coefficients for the best-fit line at the critical point are determined the following: α= (Σy)(Σ x?) - (Σπ;)(Συ;3;) ηΣ - (Συ;)2 nΣviyi - (Σα;)(Σyi) nΣx - (Σπ;)2 ότη

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. Given a bivariate data set {xi,yi}=1, we will form the best-fit linear regression model j = a + bx by the Method of Least Squares. Recall that the total error is n n - Σ(y: - 9:) = Σ(yi - a - bx;) — i=1 i=1 a= E = Show that the coefficients for the best-fit line at the critical point are determined the following: (Συ:)(Σ x?) - (Σχ.)(Σαϊy;) - (Σπ.)2 nΣ b= n ηΣ iyi - (Σπ;)(Σyi) nΣ - (Σχ.)2
Transcribed Image Text:5. Given a bivariate data set {xi,yi}=1, we will form the best-fit linear regression model j = a + bx by the Method of Least Squares. Recall that the total error is n n - Σ(y: - 9:) = Σ(yi - a - bx;) — i=1 i=1 a= E = Show that the coefficients for the best-fit line at the critical point are determined the following: (Συ:)(Σ x?) - (Σχ.)(Σαϊy;) - (Σπ.)2 nΣ b= n ηΣ iyi - (Σπ;)(Σyi) nΣ - (Σχ.)2
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