Consider the data. 3 12 6 20 14 55 35 60 10 25 The estimated regression equation for these data is ŷ = 70 – 3x. (a) Compute SSE, SST, and SSR using equations SSE = E(y; - ŷ)?, sST = E(y, - y)2, and SSR = E(ŷ, - )?. SSE = SST = SSR = (b) Compute the coefficient of determination rt. (Round your answer to three decimal places.) 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. 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. 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. (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)

MATLAB: An Introduction with Applications
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Consider the data.
X;
3
12
20
14
Yi
55
35
60
10
25
The estimated regression equation for these data is ŷ = 70 – 3x.
%D
(a) Compute SSE, SST, and SSR using equations SSE = E(y; - ŷ;)², SST = E(y; - y)², and SSR =
E(9; - 7)².
SSE =
SST =
SSR =
(b) Compute the coefficient of determination r. (Round your answer to three decimal places.)
,2 =
%D
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 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.
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.
(c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
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Transcribed Image Text:Consider the data. X; 3 12 20 14 Yi 55 35 60 10 25 The estimated regression equation for these data is ŷ = 70 – 3x. %D (a) Compute SSE, SST, and SSR using equations SSE = E(y; - ŷ;)², SST = E(y; - y)², and SSR = E(9; - 7)². SSE = SST = SSR = (b) Compute the coefficient of determination r. (Round your answer to three decimal places.) ,2 = %D 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 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. 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. (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.) Need Help? Read It
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