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The following estimated regression equation based on 10 observations was presented. ŷ 29.1290 + 0.5806x1 +0.4380x2 The values of SST and SSR are 6,716.125 and 6,228.375, respectively. (a) Find SSE. SSE = (b) Compute R². (Round your answer to three decimal places.) R² 2. (c) Compute R₂². (Round your answer to three decimal places.) Ra (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 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 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 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 provided a good fit as a large proportion of the variability in y has been explained by the estimated regression equation.

The following estimated regression equation based on 10 observations was presented. ŷ 29.1290 + 0.5806x1 +0.4380x2 The values of SST and SSR are 6,716.125 and 6,228.375, respectively. (a) Find SSE. SSE = (b) Compute R². (Round your answer to three decimal places.) R² 2. (c) Compute R₂². (Round your answer to three decimal places.) Ra (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 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 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 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 provided a good fit as a large proportion of the variability in y has been explained by the estimated regression equation.

Advanced Engineering Mathematics
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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The following estimated regression equation based on 10 observations was presented. ŷ 29.1290 + 0.5806x1 +0.4380x2 The values of SST and SSR are 6,716.125 and 6,228.375, respectively. (a) Find SSE. SSE = (b) Compute R². (Round your answer to three decimal places.) R² 2. (c) Compute R₂². (Round your answer to three decimal places.) Ra (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 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 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 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 provided a good fit as a large proportion of the variability in y has been explained by the estimated regression equation.
Transcribed Image Text:The following estimated regression equation based on 10 observations was presented. ŷ 29.1290 + 0.5806x1 +0.4380x2 The values of SST and SSR are 6,716.125 and 6,228.375, respectively. (a) Find SSE. SSE = (b) Compute R². (Round your answer to three decimal places.) R² 2. (c) Compute R₂². (Round your answer to three decimal places.) Ra (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 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 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 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 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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