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Consider the data. x, 1 3. 4 Y:4 S 6 10 12 The estimated regression equation for these data is ý 2.60 + 1.80x. (a) Compute SSE, SST, and SSR using equations SSE -E(y - ), sST - Ily, - v, and SSR - I, - . SSE 76 SST = 40 SSR = 324 (b) Compute the coefficient of determination . Comment on the goodness of tit. (For purposes of this exercise, consider a proportion large if it is at least 0.5s.) 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 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 ine provided a good fit as a small proportion of the variabity in y has been explained by the least squares ine The least squares line provided a good fit as a large proportion of the varability 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, 1 3. 4 Y:4 S 6 10 12 The estimated regression equation for these data is ý 2.60 + 1.80x. (a) Compute SSE, SST, and SSR using equations SSE -E(y - ), sST - Ily, - v, and SSR - I, - . SSE 76 SST = 40 SSR = 324 (b) Compute the coefficient of determination . Comment on the goodness of tit. (For purposes of this exercise, consider a proportion large if it is at least 0.5s.) 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 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 ine provided a good fit as a small proportion of the variabity in y has been explained by the least squares ine The least squares line provided a good fit as a large proportion of the varability 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, X, 1 4 Y, 4 8 6 10 12 The estimated regression equation for these data is ý- 2.60 + 1.80x. (a) Compute SSE, SST, and SSR using equations SSE - I(r, - ), sST - E(y, - v), and SSR = E(, - . SSE 7.6 SST =40 SSR = 324 (b) Compute the coefficient of determination ?. 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 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 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 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 ine. (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
Transcribed Image Text:Consider the data, X, 1 4 Y, 4 8 6 10 12 The estimated regression equation for these data is ý- 2.60 + 1.80x. (a) Compute SSE, SST, and SSR using equations SSE - I(r, - ), sST - E(y, - v), and SSR = E(, - . SSE 7.6 SST =40 SSR = 324 (b) Compute the coefficient of determination ?. 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 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 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 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 ine. (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
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