Consider the data. x₁ 1 2 3 4 Y₁ 4 7 5 4 12 15 The estimated regression equation for these data is ŷ = 0.30 + 2.70x. (a) Compute SSE, SST, and SSR using equations SSE = (y₁ - y)², SST = (y₁ - y)², and SSR = (₁ - y)². SSE = SST = SSR = (b) Compute the coefficient of determination ². 2²= 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 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. 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 provided 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.)
Consider the data. x₁ 1 2 3 4 Y₁ 4 7 5 4 12 15 The estimated regression equation for these data is ŷ = 0.30 + 2.70x. (a) Compute SSE, SST, and SSR using equations SSE = (y₁ - y)², SST = (y₁ - y)², and SSR = (₁ - y)². SSE = SST = SSR = (b) Compute the coefficient of determination ². 2²= 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 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. 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 provided 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.)
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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Transcribed Image Text:Consider the data.
X;
(c)
Yi
=
1
4
2 3
7 4
4 5
The estimated regression equation for these data is ŷ = 0.30 +2.70x.
-
(a) Compute SSE, SST, and SSR using equations SSE = Σ(y; – ŷ¡)², SST = Σ(y; − >)², and SSR =
SSE =
SST =
SSR =
12 15
(b) Compute the coefficient of determination r².
12 :
= Σ(ŷ; - y)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 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.
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.
Compute the sample correlation coefficient. (Round your answer to three decimal places.)
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