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Thane Company Is Interested in Establishing the Relationship Between Electricity

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Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow:
MonthMachine HoursElectricity Costs January 2,500$18,400 February 2,90021,000 March 1,90013,500 April 3,10023,000 May 3,80028,250 June 3,30022,000 July 4,10024,750 August 3,50022,750 September 2,00015,500 October 3,70026,000 November 4,70031,000 December 4,20027,750\begin{array}{lll}\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\\\text { January } & 2,500 & \$ 18,400 \\\text { February } & 2,900 & 21,000 \\\text { March } & 1,900& 13,500 \\\text { April } & 3,100 & 23,000 \\\text { May } & 3,800& 28,250 \\\text { June } & 3,300& 22,000 \\\text { July } & 4,100 & 24,750\\\text { August } & 3,500 & 22,750 \\\text { September } & 2,000 & 15,500 \\\text { October } & 3,700 & 26,000 \\\text { November } & 4,700 & 31,000 \\\text { December } & 4,200 & 27,750\end{array}



 Summary Output  Regression Statistics  Multiple R 0.965 R Squuare 0.932 Adjusted R 20.925 Standard Error 1,425.18 Observations 12.00\begin{array}{c} { \text { Summary Output } } \\ { \text { Regression Statistics } } \\\begin{array}{ | l | c | } \hline \text { Multiple R } & 0.965 \\\hline \text { R Squuare } & 0.932 \\\hline \text { Adjusted R } ^2 & 0.925 \\\hline \text { Standard Error } & 1,425.18 \\\hline \text { Observations } & 12.00 \\\hline\end{array}\end{array}


 Standard  Lower  Upper  Coefficients  Error  t Stat  P-value 95%95% Intercept 3,726.881,682.822.210.05(22.69) 7,476.45 Machine 5.770.4911.70.004.676.87 Hours \begin{array}{|l|r|r|r|r|r|r|}\hline && \text { Standard } & && \text { Lower } & \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text { P-value } & 95 \% & 95 \% \\ \hline \text { Intercept } & 3,726.88 & 1,682.82 & 2.21 & 0.05 & (22.69) & 7,476.45 \\\hline \text { Machine } & 5.77 & 0.49 & 11.7 & 0.00 & 4.67 & 6.87 \\\text { Hours } & & & & & \\\hline\end{array}


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If the controller uses regression analysis to estimate costs, the estimate of the variable portion of electricity costs is:


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