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 CoefficientsP-valueLower 95%Upper 95%Multiple R0.8160Intercept140390.30-15201.5543279.93R Square0.6659Ave Temp930.0127.36158.21Adjusted R Sq.0.5406Work Days4460.55-1199.892092.17Standard Error1644.2626Tons-270.81-278.03224.02 (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.

In the study, the facility manager at a pharmaceutical company wants to build a regression model to forecast monthly electricity cost with three variables average outdoor temperature, working days per month, tons of product produced. The regression output is given.(a) From the output, the coefficient of variation(R square) is 0.6659. Thus, the proportion in variation in electricity cost is explained by variation in the explanatory variables is 0.6659.(b) The hypothesis is, Null hypothesis: H0:βWork days=0 Alternative hypothesis: H1:βWork days≠0 From the output, the P-value corresponding to working days per month is 0.55. Since the P-value is greater than the level of significance indicating that the null hypothesis is not rejected. Thus, the working days per month is not a significant predictor of electricity costs at the 5% significant level.(c) The regression equation is, Electricity costs=14039+93Ave Temp+446Work Days-27Tons. The estimated electricity costs in a month with an average outdoor temperature of 68 degrees, 20 working days, and 3 tons of product produced is, Electricity costs=14039+9368+44620-273=14039+6324+8920-81=29202 Thus, the estimated electricity costs in a month with an average outdoor temperature of 68 degrees, 20 working days, and 3 tons of product produced is 29,202.

Answered: The facility manager at a…

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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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