(a) Compute SE, SST, and SSR using equations SSE = E(y, - ŷ,)², sST = E(y, - y)², and SSR = E(ŷ, - y)?. 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.) 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. 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 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. (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
y; 3 7 4
10
12
The estimated regression equation for these data is ý = 0.90 + 2.10x.
(a) Compute SSE, SST, and SSR using equations SSE = E(y, - ý,)?, sST = E(y, - y)?, and SSR = E(ŷ, - y)?.
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.)
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.
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 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.
(c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
Transcribed Image Text:Consider the data. X; 1 2 3 4 y; 3 7 4 10 12 The estimated regression equation for these data is ý = 0.90 + 2.10x. (a) Compute SSE, SST, and SSR using equations SSE = E(y, - ý,)?, sST = E(y, - y)?, and SSR = E(ŷ, - y)?. 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.) 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. 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 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. (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
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