The facility manager at a pharmaceutical company wants to build a regression model to forecast monthly electricity cost. Three main variables are thought to dictate electricity cost: average outdoor temperature, working days per month, tons of product produced. A portion of the regression output is included below. Regression Statistics Coefficients P-value Lower 95% Upper 95% Multiple R 0.8160 Intercept 14039 0.30 -15201.55 43279.93 R Square 0.6659 Ave Temp 93 0.01 27.36 158.21 Adjusted R Sq. 0.5406 Work Days 446 0.55 -1199.89 2092.17 Standard Error 1644.2626 Tons -27 0.81 -278.03 224.02 (a) What proportion in variation in electricity cost is explained by variation in the explanatory variables? (b) At the 5% significant level, is working days per month a significant predictor of electricity costs? Explain briefly. (c) Estimate the electricity costs in a month with an average outdoor temperature of 68 degrees, 20 working days, and 3 tons of product produced.
The facility manager at a pharmaceutical company wants to build a regression model to forecast monthly electricity cost. Three main variables are thought to dictate electricity cost: average outdoor temperature, working days per month, tons of product produced. A portion of the regression output is included below.
|
Regression Statistics |
|
Coefficients |
P-value |
Lower 95% |
Upper 95% |
|
|
Multiple R |
0.8160 |
Intercept |
14039 |
0.30 |
-15201.55 |
43279.93 |
|
R Square |
0.6659 |
Ave Temp |
93 |
0.01 |
27.36 |
158.21 |
|
Adjusted R Sq. |
0.5406 |
Work Days |
446 |
0.55 |
-1199.89 |
2092.17 |
|
Standard Error |
1644.2626 |
Tons |
-27 |
0.81 |
-278.03 |
224.02 |
(a) What proportion in variation in electricity cost is explained by variation in the explanatory variables?
(b) At the 5% significant level, is working days per month a significant predictor of electricity costs? Explain briefly.
(c) Estimate the electricity costs in a month with an average outdoor temperature of 68 degrees, 20 working days, and 3 tons of product produced.
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