Yi SSE = SST = 4 2 3 8 4 4 5 12 12 he estimated regression equation for these data is ŷ = 2.00 +2.00x. a) Compute SSE, SST, and SSR using equations SSE = (y₁ - ₁)², SST = (y₁ - y)², and SSR => = Σ(ŷ₁ - ) ².
Yi SSE = SST = 4 2 3 8 4 4 5 12 12 he estimated regression equation for these data is ŷ = 2.00 +2.00x. a) Compute SSE, SST, and SSR using equations SSE = (y₁ - ₁)², SST = (y₁ - y)², and SSR => = Σ(ŷ₁ - ) ².
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;
Yi
1 2 3
4
8 4
4 5
12 12
The estimated regression equation for these data is ŷ = 2.00 + 2.00x.
(a) Compute SSE, SST, and SSR using equations SSE = (y; - ;)², SST = ²(y; - >², and SSR =
SSE =
SST =
SSR =
(b) Compute the coefficient of determination ².
₁²=
= Σ(y₁ - y)².
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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