X; 2 5 Y, 4 86 10 12 The estimated regression equation for these data is ý = 2.60 + 1.80x. (a) Compute SSE, SST, and SSR using equations SSE - 2(y, - ŷ², sST = 2(y, - 7)², and SSR = 1(9, - 7². 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.) 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 lea O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squ O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squa 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 lea (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
X; 2 5 Y, 4 86 10 12 The estimated regression equation for these data is ý = 2.60 + 1.80x. (a) Compute SSE, SST, and SSR using equations SSE - 2(y, - ŷ², sST = 2(y, - 7)², and SSR = 1(9, - 7². 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.) 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 lea O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squ O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squa 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 lea (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
q19
![Consider the data.
X;
1
2
3
4
4
8
6
10
12
The estimated regression equation for these data is ý = 2.60 + 1.80x.
(a) Compute SSE, ST, and SSR using equations SSE = I(y, - 9)?, SST = I(y, - y)?, and SSR = I(9, - 7)?.
SSE =
SST =
SSR =
(b) Compute the coefficient of determination r2.
2 =
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 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 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 small 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.)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F846a1c78-ea58-4624-9cf5-412b48970cdc%2F3a829fb4-e422-4c5e-8de1-e7af1bb1ce1c%2Fgoyujs_processed.png&w=3840&q=75)
Transcribed Image Text:Consider the data.
X;
1
2
3
4
4
8
6
10
12
The estimated regression equation for these data is ý = 2.60 + 1.80x.
(a) Compute SSE, ST, and SSR using equations SSE = I(y, - 9)?, SST = I(y, - y)?, and SSR = I(9, - 7)?.
SSE =
SST =
SSR =
(b) Compute the coefficient of determination r2.
2 =
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 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 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 small 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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