Consider the data. 12 20 x; 3 14 Y, 65 40 60 | 10 20 The estimated regression equation for these data is ý = 77.5 - 3.5x. (a) Compute SSE, SST, and SSR using equations SSE = E(y, - ŷ,)², sST = E(y, - V², and SSR = E(9, - y)². SSE = SST = SSR = (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) 2 = 0.05 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 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 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 small proportion of the variability in y has been explained by the least squares line.

Consider the data. 12 20 x; 3 14 Y, 65 40 60 | 10 20 The estimated regression equation for these data is ý = 77.5 - 3.5x. (a) Compute SSE, SST, and SSR using equations SSE = E(y, - ŷ,)², sST = E(y, - V², and SSR = E(9, - y)². SSE = SST = SSR = (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) 2 = 0.05 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 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 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 small proportion of the variability in y has been explained by the least squares line.

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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