Consider the data. x; 2 3 4 5. Y; 2 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 = I(y, - p?, sST = E(y, - )?, and SSR = E(9, - ?. SSE = SST = SSR = (b) Compute the coefficient of determination r. 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 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. (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)

MATLAB: An Introduction with Applications
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Chapter1: Starting With Matlab
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Consider the data.
x;
1
2
3
4
5
11
12
The estimated regression equation for these data is ý = 1.50 2.10x.
(a) Compute SSE, SST, and SSR using equations SSE = I(y, - p?, sST = E(y, - v)?, and SSR = E(9, - ?.
SSE =
SST =
SSR =
(b) Compute the coefficient of determination r.
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 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.
(c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
Transcribed Image Text:Consider the data. x; 1 2 3 4 5 11 12 The estimated regression equation for these data is ý = 1.50 2.10x. (a) Compute SSE, SST, and SSR using equations SSE = I(y, - p?, sST = E(y, - v)?, and SSR = E(9, - ?. SSE = SST = SSR = (b) Compute the coefficient of determination r. 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 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. (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
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