The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature (x1), the number of days in the month (x2), the average product purity (x3), and the tons of product produced (x4). A multiple linear regression analysis was applied to the experimental data using MINTAB and the following results were obtained: Regression Analysis: y versus x1, x2, x3, x4 Regression Equation y- 123 + 0.757 x1 + 7.52 x2 + 2.48 x3 - 0481 x4 Coefficients Term Coef SE Coef T-Value P-Value VIF Constant -123 157 -0.78 0.459 x1 0.757 0279 2.71 0.030 232 x2 7.52 4.01 1.87 0.103 216 x3 2.48 1.81 1.37 0.212 134 x4 -0.481 0555 -0.87 0415 1.01 Analysis of Variance Source DF Seg SS Seg MS F-Value P-Value Regression 4 5600.5 1400.1 10.08 0.005 Error 972.5 138.9 Total 11 6572.9 Then, an estimate of the error variance is equal to a. 157 b. 6572.9 C. 972.5 d. 138.9 e. 0.005

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mester 2021
English (en) -
The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature (x1), the number of days in the month
(x2), the average product purity (x3), and the tons of product produced (x4). A multiple linear regression analysis was applied to the experimental data using
MINTAB and the following results were obtained:
Regression Analysis: y versus x1, x2, x3, x4
Regression Equation
y - -123 + 0.757 x1 + 7.52 x2+ 248 x3 - 0.481 x4
Coefficients
Term
Coef SE Coef T-Value P-Value
VIF
Constant
-123
157
-0.78
0.459
x1
0.757
0.279
2.71
0.030 232
x2
7.52
4.01
1.87
0.103 216
x3
2.48
1.81
1.37
0.212 1,34
x4
-0.481
0.555
-0.87
0.415 1.01
Analysis of Variance
Source
DF Seg SS Seg MS F-Value P-Value
Regression
4 5600.5
1400.1
10.08
0.005
Error
972.5
138.9
Total
11 6572.9
Then, an estimate of the error variance is equal to
a.
157
b. 6572.9
C.
972.5
d. 138.9
e.
0.005
The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature (x1), the number of days in the month
(x2), the average product purity (x3), and the tons of product produced (x4). A multiple linear regression analysis was applied to the experimental data using
Transcribed Image Text:mester 2021 English (en) - The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature (x1), the number of days in the month (x2), the average product purity (x3), and the tons of product produced (x4). A multiple linear regression analysis was applied to the experimental data using MINTAB and the following results were obtained: Regression Analysis: y versus x1, x2, x3, x4 Regression Equation y - -123 + 0.757 x1 + 7.52 x2+ 248 x3 - 0.481 x4 Coefficients Term Coef SE Coef T-Value P-Value VIF Constant -123 157 -0.78 0.459 x1 0.757 0.279 2.71 0.030 232 x2 7.52 4.01 1.87 0.103 216 x3 2.48 1.81 1.37 0.212 1,34 x4 -0.481 0.555 -0.87 0.415 1.01 Analysis of Variance Source DF Seg SS Seg MS F-Value P-Value Regression 4 5600.5 1400.1 10.08 0.005 Error 972.5 138.9 Total 11 6572.9 Then, an estimate of the error variance is equal to a. 157 b. 6572.9 C. 972.5 d. 138.9 e. 0.005 The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature (x1), the number of days in the month (x2), the average product purity (x3), and the tons of product produced (x4). A multiple linear regression analysis was applied to the experimental data using
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