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Could you please help on the following question.
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Question 1 As the accountant for Irish Linens Inc, you have been analyzing the accounts to determine what activity drives the manufacturing overhead costs. The previous accountant used direct labour hours, as she believed it was the most appropriate driver. However, the company has gone through significant automation, and you believe that machine hours may be the better driver. The following information has been gathered: Machine Manufacturing Month hours overhead January 4,580 $1,067,869 February 4,503 1,074,792 March 4,365 1,068,344 April 4,134 1,044, 164 May 3,927 1,022,968 June 3,804 1,037,620 July 3,957 1,042,640 August 4,158 1,032,056 September 4,029 1,051,244 October 4,245 1,054,024 November 4,469 1,063,254 December 4,692 1,072, 484 Using 20 observations including the above, you have run a regression analysis with the following Summary outputs: SUMMARY OUTPUT Regression Statistics Multiple R 0.864101825 R Square 0.746671964 Adjusted R Squa 0.72133916 Standard Error 8977.012134 Observations 20 ANOVA dif SS MS F Significance F Regression 1 2,375,254,842 2,375,254,842 29.47450958 0.000289286 Residual 10 805,867,469 80,586,747 Tota 11 3,181,122,311 Coefficients Standard Error Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 833379.2593 40466.29719 20.59440367 1.61144E-09 743214.7303 923543.7883 743214.7303 923543.7883 X Variable 1 51.72537774 9.527531144 5.429043155 0.000289286 30.49671544 72.95404005 30.49671544 72.95404005In addition, a similar analysis has been done on direct labour hours: Direct labour Manufacturing Month hours overhead January 7,802 $1,067,869 February 6,509 1,074,792 March 7,195 1,068,344 Apri 6,502 1,044,164 May 5.885 1,022,968 June 5,512 1,037,620 July 6,081 1,042,640 August 6,578 1,032,056 September 6,198 1,051,244 October 6,894 1,054,024 November 7,517 1,063,254 December 7,976 1,072,484 Using 20 observations including the above information, the regression analysis on direct labour hours produced a summary output as follows: SUMMARY OUTPUT Regression Statistics Multiple R 0.743890262 R Square 0.553372722 Adjusted R Squa 0.508709994 Standard Error 11919.63086 Observations 20 ANOVA dif SS MS F Significance F Regression 1 1,760,346,312 1,760,346,312 12.39003413 0.005538914 Residual 10 1,420,775,999 142,077,600 Total 11 3,181,122,311 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 942768.7021 31397.78039 30.02660349 3.92718E-11 872810.0877 1012727.316 872810.0877 1012727.316 X Variable 1 16.34533069 4.643628416 3.51994803 0.005538914 5.998681805 26.69197958 5.998681805 26.69197958 An analysis of the nature of the various accounts that make up the manufacturing overhead was undertaken for December: Item Amount Cost classification Factory property tax $ 349,865 Fixed Factory janitorial services 61,387 Variable Machine depreciation 250,375 Fixed Supervisory salaries 200,000 Fixed Indirect supplies 10,855 Variable Wages for supply personnel 102,000 Variable Hydro - variable component 73,002 Variable Hydro - fixed component 25,000 Fixed $ 1,072,484Required: a) Develop an estimate of the cost function for manufacturing overhead using the high- low method for both (i) machine hours and (ii) direct labour hours. Note: Provide the variable cost component to four decimal places. (4 marks) b) Develop an estimate of the cost function for manufacturing overhead using account analysis and direct labour hours. Note: Provide the variable cost component to four decimal places. (2 marks) c) Develop an estimate of the cost function for manufacturing overhead using regression analysis. Choose the best cost driver labour hours or machine hours to develop the cost function and explain your choice. Note: Provide the variable cost component to four decimal places. (4 marks) d) Which one of the estimates you developed (a), (b), or (c) is the most reliable? Present your explanations. (2 marks)
