In a regression analysis of a first-order model involving 3 predictor variables and 24 observations, the following estimated regression equation was developed. ŷ = 12 – 15x, + 5x2 + 16x3 Also, the following standard errors and the sum of squares were obtained. S6. = 3 SST = 4,800 = 7 Sb3 = 6 SSE = 1,295 Find the multiple coefficient of determination. O 0.27 O 0.37 O 0.73 O 0.85
In a regression analysis of a first-order model involving 3 predictor variables and 24 observations, the following estimated regression equation was developed. ŷ = 12 – 15x, + 5x2 + 16x3 Also, the following standard errors and the sum of squares were obtained. S6. = 3 SST = 4,800 = 7 Sb3 = 6 SSE = 1,295 Find the multiple coefficient of determination. O 0.27 O 0.37 O 0.73 O 0.85
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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![In a regression analysis of a first-order model involving 3 predictor variables and 24 observations, the following estimated regression equation was developed.
\[
\hat{y} = 12 - 15x_1 + 5x_2 + 16x_3
\]
Also, the following standard errors and the sum of squares were obtained.
\[
s_{b_1} = 3 \quad s_{b_2} = 6 \quad s_{b_3} = 7
\]
\[
SST = 4,800 \quad SSE = 1,295
\]
Find the multiple coefficient of determination.
- ○ 0.27
- ○ 0.37
- ○ 0.73
- ○ 0.85](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F358923dd-976b-4983-a367-bafd3b4b55c1%2Ff68ce74a-fefd-4c64-82e7-fd9de8aa8186%2Fgw9ghyx_processed.png&w=3840&q=75)
Transcribed Image Text:In a regression analysis of a first-order model involving 3 predictor variables and 24 observations, the following estimated regression equation was developed.
\[
\hat{y} = 12 - 15x_1 + 5x_2 + 16x_3
\]
Also, the following standard errors and the sum of squares were obtained.
\[
s_{b_1} = 3 \quad s_{b_2} = 6 \quad s_{b_3} = 7
\]
\[
SST = 4,800 \quad SSE = 1,295
\]
Find the multiple coefficient of determination.
- ○ 0.27
- ○ 0.37
- ○ 0.73
- ○ 0.85
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