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Consider the data. x 12 3 4 Y₁ 4 5 7 S 11 15 The estimated regression equation for these data is 9-0.60 +2.60x. (a) Compute SSE, SST, and SSR using equations SSE = X(Y/-912, SST - Xy/-)², and SSR-9/-)². SSE- SST - SSR- (b) Compute the coefficient of determination 2. 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 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. 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. 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. x 12 3 4 Y₁ 4 5 7 S 11 15 The estimated regression equation for these data is 9-0.60 +2.60x. (a) Compute SSE, SST, and SSR using equations SSE = X(Y/-912, SST - Xy/-)², and SSR-9/-)². SSE- SST - SSR- (b) Compute the coefficient of determination 2. 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 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. 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. 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. X1 23 4 5 Y₁ 475 11 15 The estimated regression equation for these data is 9-0.60 +2.60x. (a) Compute SSE, SST, and SSR using equations SSE = X(Y/-)², SST - 2y/-)2, and SSR = X(9/-)². - SSE- SST - SSR- (b) Compute the coefficient of determination 2. 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 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. 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. 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. X1 23 4 5 Y₁ 475 11 15 The estimated regression equation for these data is 9-0.60 +2.60x. (a) Compute SSE, SST, and SSR using equations SSE = X(Y/-)², SST - 2y/-)2, and SSR = X(9/-)². - SSE- SST - SSR- (b) Compute the coefficient of determination 2. 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 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. 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. 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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