The following estimated regression equation based on 10 observations was presented. ý = 29.1270 + 0.5106Xq + 0.4980X2 The values of SST and SSR are 6,714.125 and 6,225.375, respectively. (a) Find SSE. SSE = (b) Compute R². (Round your answer to three decimal places.) R² = (c) Compute R₂². (Round your answer to three decimal places.) R₂2= = (d) Comment on the goodness of fit (For purposes of this exercise consider a proportion large it
The following estimated regression equation based on 10 observations was presented. ý = 29.1270 + 0.5106Xq + 0.4980X2 The values of SST and SSR are 6,714.125 and 6,225.375, respectively. (a) Find SSE. SSE = (b) Compute R². (Round your answer to three decimal places.) R² = (c) Compute R₂². (Round your answer to three decimal places.) R₂2= = (d) Comment on the goodness of fit (For purposes of this exercise consider a proportion large it
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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![The following estimated regression equation based on 10 observations was presented.
\[
\hat{y} = 29.1270 + 0.5106x_1 + 0.4980x_2
\]
The values of SST and SSR are 6,714.125 and 6,225.375, respectively.
(a) Find SSE.
\[
\text{SSE} = \_\_\_\_\_
\]
(b) Compute \( R^2 \). (Round your answer to three decimal places.)
\[
R^2 = \_\_\_\_\_
\]
(c) Compute \( R_a^2 \). (Round your answer to three decimal places.)
\[
R_a^2 = \_\_\_\_\_
\]
(d) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.)
- ⃝ The estimated regression equation provided a good fit as a small proportion of the variability in \( y \) has been explained by the estimated regression equation.
- ⃝ The estimated regression equation did not provide a good fit as a small proportion of the variability in \( y \) has been explained by the estimated regression equation.
- ⃝ The estimated regression equation did not provide a good fit as a large proportion of the variability in \( y \) has been explained by the estimated regression equation.
- ⃝ The estimated regression equation provided a good fit as a large proportion of the variability in \( y \) has been explained by the estimated regression equation.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ff7b76bbb-1122-4c75-9c9e-2038b7c47d4e%2F994a3edb-7398-4f49-9c95-3f7b82574181%2Fuxy6ua_processed.png&w=3840&q=75)
Transcribed Image Text:The following estimated regression equation based on 10 observations was presented.
\[
\hat{y} = 29.1270 + 0.5106x_1 + 0.4980x_2
\]
The values of SST and SSR are 6,714.125 and 6,225.375, respectively.
(a) Find SSE.
\[
\text{SSE} = \_\_\_\_\_
\]
(b) Compute \( R^2 \). (Round your answer to three decimal places.)
\[
R^2 = \_\_\_\_\_
\]
(c) Compute \( R_a^2 \). (Round your answer to three decimal places.)
\[
R_a^2 = \_\_\_\_\_
\]
(d) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.)
- ⃝ The estimated regression equation provided a good fit as a small proportion of the variability in \( y \) has been explained by the estimated regression equation.
- ⃝ The estimated regression equation did not provide a good fit as a small proportion of the variability in \( y \) has been explained by the estimated regression equation.
- ⃝ The estimated regression equation did not provide a good fit as a large proportion of the variability in \( y \) has been explained by the estimated regression equation.
- ⃝ The estimated regression equation provided a good fit as a large proportion of the variability in \( y \) has been explained by the estimated regression equation.
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