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Consider the data. X; 1 2 3 4 Y; 3 8 11 12 The estimated regression equation for these data is ý = 1.50 + 2.10x. (a) Compute SSE, SST, and SSR using equations SSE = E(y, -ŷ)², SST = E(y, - y)², and SSR = E(§, - y)². SSE = SST = SSR = b) Compute the coefficient of determination r2. Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The least sauares line did

Consider the data. X; 1 2 3 4 Y; 3 8 11 12 The estimated regression equation for these data is ý = 1.50 + 2.10x. (a) Compute SSE, SST, and SSR using equations SSE = E(y, -ŷ)², SST = E(y, - y)², and SSR = E(§, - y)². SSE = SST = SSR = b) Compute the coefficient of determination r2. Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The least sauares line did

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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Consider the data. 1 4 Y; 3 8 11 12 The estimated regression equation for these data is ŷ = 1.50 + 2.10x. (a) Compute SSE, SST, and SSR using equations SSE = E(yi - ŷ)², SST = E(y, – y)², and SSR = SSE = SST = SSR = (b) Compute the coefficient of determination r2. r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
Transcribed Image Text:Consider the data. 1 4 Y; 3 8 11 12 The estimated regression equation for these data is ŷ = 1.50 + 2.10x. (a) Compute SSE, SST, and SSR using equations SSE = E(yi - ŷ)², SST = E(y, – y)², and SSR = SSE = SST = SSR = (b) Compute the coefficient of determination r2. r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
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The estimated regression equation is 

y^= 1.50+2.10x. 

 

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