1. (10 points) Data from various public electricity supply authorities was collected. The data include the following variables: X1, the capital costs. X2, the labor costs. X3, the energy costs. Y, the output of electricity in millions of kilowatts, (dependent variable) We will estimate a model of the form: Given below is the regression output SUMMARY OUTPUT Regression Statistics Y=A0+A1 X1+A2 * X2 + A3X3. Multiple R 0.210124 R Square 0.044152 Adjusted R Square 0.05481 Standard Error 610.4604 Observations 16 ANOVA Regression Residual Total Df SS 3 MS F Significance F 206566.6 68855.54 0.184767 0.904708 12 4471943 372661.9 15 4678510 Coefficients Intercept 287.8562 X1 -0.65164 X2 X3 119.6728 -2.09825 Standard Error t Stat P-value 291.8021 0.986478 0.343372 3.062604 -0.21277 0.835074 557.4253 0.214689 0.833615 Lower 95% -347.926 -7.32449 6.021195 Upper 95% 923.6383 -1094.85 1334.198 13.87221 -0.15126 0.882287 -32.3232 28.12669 (a) Which independent variables have a significant coefficient and therefore will have a significant impact on the output of electricity? (b) Do the above results indicate that the data fits the model well and therefore can the results of this regression be used to make business decisions about changing the output of electricity? Please explain.
1. (10 points) Data from various public electricity supply authorities was collected. The data include the following variables: X1, the capital costs. X2, the labor costs. X3, the energy costs. Y, the output of electricity in millions of kilowatts, (dependent variable) We will estimate a model of the form: Given below is the regression output SUMMARY OUTPUT Regression Statistics Y=A0+A1 X1+A2 * X2 + A3X3. Multiple R 0.210124 R Square 0.044152 Adjusted R Square 0.05481 Standard Error 610.4604 Observations 16 ANOVA Regression Residual Total Df SS 3 MS F Significance F 206566.6 68855.54 0.184767 0.904708 12 4471943 372661.9 15 4678510 Coefficients Intercept 287.8562 X1 -0.65164 X2 X3 119.6728 -2.09825 Standard Error t Stat P-value 291.8021 0.986478 0.343372 3.062604 -0.21277 0.835074 557.4253 0.214689 0.833615 Lower 95% -347.926 -7.32449 6.021195 Upper 95% 923.6383 -1094.85 1334.198 13.87221 -0.15126 0.882287 -32.3232 28.12669 (a) Which independent variables have a significant coefficient and therefore will have a significant impact on the output of electricity? (b) Do the above results indicate that the data fits the model well and therefore can the results of this regression be used to make business decisions about changing the output of electricity? Please explain.
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
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Transcribed Image Text:1. (10 points) Data from various public electricity supply authorities was collected.
The data include the following variables:
X1, the capital costs.
X2, the labor costs.
X3, the energy costs.
Y, the output of electricity in millions of kilowatts, (dependent variable)
We will estimate a model of the form:
Given below is the regression output
SUMMARY OUTPUT
Regression Statistics
Y=A0+A1 X1+A2 * X2 + A3X3.
Multiple R
0.210124
R Square
0.044152
Adjusted R
Square
0.05481
Standard Error
610.4604
Observations
16
ANOVA
Regression
Residual
Total
Df
SS
3
MS
F
Significance
F
206566.6 68855.54 0.184767
0.904708
12
4471943 372661.9
15
4678510
Coefficients
Intercept
287.8562
X1
-0.65164
X2
X3
119.6728
-2.09825
Standard
Error
t Stat P-value
291.8021 0.986478 0.343372
3.062604 -0.21277 0.835074
557.4253 0.214689 0.833615
Lower 95%
-347.926
-7.32449 6.021195
Upper
95%
923.6383
-1094.85 1334.198
13.87221 -0.15126 0.882287
-32.3232 28.12669
(a) Which independent variables have a significant coefficient and therefore will have a
significant impact on the output of electricity?
(b) Do the above results indicate that the data fits the model well and therefore can the results of this
regression be used to make business decisions about changing the output of electricity? Please
explain.
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