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Consider the data. X₁ Y₁ 1 2 3 4 5 486 10 12 The estimated regression equation for these data is 9 = 2.60 + 1.50x. (a) Compute SSE, SST, and SSR using equations SSE - (-9), SST = (-) and SSR = I(-y)² SSE- SST- SSR- (b) Compute the coefficient of determination P- 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 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 small 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. O 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.)

Consider the data. X₁ Y₁ 1 2 3 4 5 486 10 12 The estimated regression equation for these data is 9 = 2.60 + 1.50x. (a) Compute SSE, SST, and SSR using equations SSE - (-9), SST = (-) and SSR = I(-y)² SSE- SST- SSR- (b) Compute the coefficient of determination P- 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 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 small 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. O 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.)

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 2 3 4 5 Y₁ 4 8 6 10 12 x₂ The estimated regression equation for these data is ý = 2.60+ 1.80x (a) Compute SSE, SST, and SSR using equations SSE-(y, -9), SST(Y,-), and SSR = = 2(9,- y)². SSE SST - SSR- (b) Compute the coefficient of determination 2 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 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 small proportion of the variability in y has been explained by the least squares line. 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. O 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 2 3 4 5 Y₁ 4 8 6 10 12 x₂ The estimated regression equation for these data is ý = 2.60+ 1.80x (a) Compute SSE, SST, and SSR using equations SSE-(y, -9), SST(Y,-), and SSR = = 2(9,- y)². SSE SST - SSR- (b) Compute the coefficient of determination 2 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 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 small proportion of the variability in y has been explained by the least squares line. 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. O 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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