Next, we investigate whether the energy production mainly depends on the electricity supplied.      To examine that we carry out a regression model and the results are given in Exhibit 11 below.       We define the variables as follows:       Dependent variable: total_energy = is the total energy production of different sources (GWh)       Independent variable: Electricity supplied = monthly electricity supplied (GWh)   i)  From Exhibit 11, state the intercept coefficient value.     Is it meaningful? Explain.         ii) Give an interpretation of the slope coefficient given in Exhibit 11.         iii) From the output in the Exhibit 11 above, test if the monthly electricity supply significantly increases the energy production in Australia.              Show all the steps and use the p-value approach with 1% level of significance

College Algebra
1st Edition
ISBN:9781938168383
Author:Jay Abramson
Publisher:Jay Abramson
Chapter6: Exponential And Logarithmic Functions
Section6.8: Fitting Exponential Models To Data
Problem 3SE: What is regression analysis? Describe the process of performing regression analysis on a graphing...
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b)  Next, we investigate whether the energy production mainly depends on the electricity supplied.
     To examine that we carry out a regression model and the results are given in Exhibit 11 below.

      We define the variables as follows:

      Dependent variable: total_energy = is the total energy production of different sources (GWh)

      Independent variable: Electricity supplied = monthly electricity supplied (GWh)

 

i)  From Exhibit 11, state the intercept coefficient value.

    Is it meaningful? Explain.        

ii) Give an interpretation of the slope coefficient given in Exhibit 11.        

iii) From the output in the Exhibit 11 above, test if the monthly electricity supply significantly increases the energy production in Australia.

             Show all the steps and use the p-value approach with 1% level of significance.   

Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
0.38890
0.15124
0.15100
7263.6682
3446
df
1
3444
3445
Exhibit 11
SS
MS
3.238E+10 3E+10
1.817E+11 5E+07
2.141E+11
F
613.691
Coefficients Standard Error t Stat P-value
Intercept
5664.6823
126.6363
44.732
Electricity supplied 14744.4412
595.1869
24.773 8.11E-125
0
Significance F
8.1E-125
Lower 95% Upper 95%
5416.393 5912.972
13577.49
15911.4
Transcribed Image Text:Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total 0.38890 0.15124 0.15100 7263.6682 3446 df 1 3444 3445 Exhibit 11 SS MS 3.238E+10 3E+10 1.817E+11 5E+07 2.141E+11 F 613.691 Coefficients Standard Error t Stat P-value Intercept 5664.6823 126.6363 44.732 Electricity supplied 14744.4412 595.1869 24.773 8.11E-125 0 Significance F 8.1E-125 Lower 95% Upper 95% 5416.393 5912.972 13577.49 15911.4
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