Question

In: Accounting

You have recently been hired as a cost accountant at Travenol Laboratories. The controller is an...

You have recently been hired as a cost accountant at Travenol Laboratories. The controller is an "old school" accountant and has heard that you recently graduated with a degree in accounting. One day he summons you to his office to assign you a task. He says, "I understand that recently educated accountants are using a variety of statistical tools to determine causality between costs and their respective drivers. We have been using direct labor hours as our cost driver for our manufacturing overhead costs for as long as I have been here. In the last few years our production processes have become more automated and I am not sure whether direct labor hours is the appropriate allocation basis for our manufacturing overhead costs. I would like you to use some of those statistical tools to determine whether there is a more appropriate cost driver."

You leave his office recognizing that this is a tremendous career opportunity. If you can convince your boss that you can use statistical analysis to determine the best cost driver, you will have established yourself in the department as a knowledgeable professional. It is good fortune that one of your projects in your cost class dealt specifically with this type of analysis.

Year MOH DLH DLS MH DMS
2000 948,768 7,595 113,932 19,650 149,712
2001 833,153 14,235 173,518 12,767 111,754
2002 753,039 14,997 184,961 12,002 126,155
2003 799,757 12,901 153,511 15,420 140,550
2004 972,624 8,555 168,322 11,107 167,648
2005 967,537 10,565 198,476 13,759 143,981
2006 945,057 12,878 153,169 19,230 110,323
2007 750,112 8,888 93,322 12,319 115,301
2008 884,112 11,287 169,311 13,489 158,897
2009 923,244 10,127 111,900 14,603 167,418
2010 929,320 11,690 215,349 12,126 120,126
2011 785,210 7,707 75,606 11,334 121,555
2012 862,449 12,182 142,734 17,987 101,168
2013 865,873 5,095 36,429 18,015 156,535
2014 804,287 11,464 211,962 15,504 152,855
2015 797,726 9,989 149,840 12,472 148,269
MOH=Manufacturing Overhead MH=Machine Hours
DLH=Direct Labor Hours DM$=Direct Material Dollars
DL$=Direct Labor Dollars

Requirement:

1. Perform a regression on DLH, DL$, MH and DM$ and comment on the following for each;
           a. The equation
           b. Goodness of fit
           c. Significance of independent variables
           d. Any autocorrelation
2. What would you recommend and why?

Solutions

Expert Solution

Year MOH DLH DLS MH DMS
2000 948,768 7,595 113,932 19,650 149,712
2001 833,153 14,235 173,518 12,767 111,754
2002 753,039 14,997 184,961 12,002 126,155
2003 799,757 12,901 153,511 15,420 140,550
2004 972,624 8,555 168,322 11,107 167,648
2005 967,537 10,565 198,476 13,759 143,981
2006 945,057 12,878 153,169 19,230 110,323
2007 750,112 8,888 93,322 12,319 115,301
2008 884,112 11,287 169,311 13,489 158,897
2009 923,244 10,127 111,900 14,603 167,418
2010 929,320 11,690 215,349 12,126 120,126
2011 785,210 7,707 75,606 11,334 121,555
2012 862,449 12,182 142,734 17,987 101,168
2013 865,873 5,095 36,429 18,015 156,535
2014 804,287 11,464 211,962 15,504 152,855
2015 797,726 9,989 149,840 12,472 148,269
MOH=Manufacturing Overhead MH=Machine Hours
DLH=Direct Labor Hours DM$=Direct Material Dollars
DL$=Direct Labor Dollars
Refression on DLH
Using Data analysis with regression tool:
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.193764
R Square 0.037544
Adjusted R Square -0.0312
Standard Error 78215.79
Observations 16
ANOVA
df SS MS F Significance F
Regression 1 3341045092 3.34E+09 0.546127 0.472113755
Residual 14 85647932515 6.12E+09
Total 15 88988977607
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 924361.6 84130.142 10.98728 2.88E-08 743920.411 1104803 743920.4 1104803
X Variable 1 -5.6861 7.694272289 -0.739 0.472114 -22.18867014 10.81648 -22.1887 10.81648
Equation:
Y=924361.6-5.6861X
Y=MOH
X=DLH
R squared=Goodness of fit=0.037544
This means only 3.75% of data fits with the regression line
Correlation coefficient=Square root of R squared=(0.037544^0.5)= 0.193764
There is very ngligible correlation between MOH and DLH
Regression on DL$
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.17708
R Square 0.031357
Adjusted R Square -0.03783
Standard Error 78466.79
Observations 16
ANOVA
df SS MS F Significance F
Regression 1 2790455798 2.79E+09 0.453214 0.511773449
Residual 14 86198521809 6.16E+09
Total 15 88988977607
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 823665.3 62890.63928 13.09679 3.02E-09 688778.342 958552.4 688778.3 958552.4
X Variable 1 0.273609 0.406423706 0.673212 0.511773 -0.598082941 1.145301 -0.59808 1.145301
Equation:
Y=823665.3+0.273609X
Y=MOH
X=DL$
R squared=Goodness of fit=0.031357
This means only 3.14% of data fits with the regression line
Correlation coefficient=Square root of R squared=(0.031357^0.5)= 0.17708
There is very ngligible correlation between MOH and DL$
Regression on MH
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.318609
R Square 0.101512
Adjusted R Square 0.037334
Standard Error 75571.89
Observations 16
ANOVA
df SS MS F Significance F
Regression 1 9033434358 9.03E+09 1.58173 0.229085936
Residual 14 79955543249 5.71E+09
Total 15 88988977607
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 739301.9 100849.6225 7.330736 3.73E-06 523000.9886 955602.8 523001 955602.8
X Variable 1 8.60041 6.838375771 1.257668 0.229086 -6.066447643 23.26727 -6.06645 23.26727
Equation:
Y=739301+8.60041X
Y=MOH
X=MH
R squared=Goodness of fit=0.101512
This means only 10.15% of data fits with the regression line
Correlation coefficient=Square root of R squared=(0.101512^0.5)= 0.318609
There is some amount of correlation between MOH and MH
Regression on DMS
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.316841
R Square 0.100388
Adjusted R Square 0.03613
Standard Error 75619.12
Observations 16
ANOVA
df SS MS F Significance F
Regression 1 8933452881 8.93E+09 1.56227 0.231824118
Residual 14 80055524726 5.72E+09
Total 15 88988977607
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 709892.4 124650.4848 5.695063 5.53E-05 442543.6714 977241.1 442543.7 977241.1
X Variable 1 1.123956 0.899231327 1.249908 0.231824 -0.804702967 3.052616 -0.8047 3.052616
Equation:
Y=709892+1.123956X
Y=MOH
X=DMS
R squared=Goodness of fit=0.100388
This means only 10.04% of data fits with the regression line
Correlation coefficient=Square root of R squared=(0.100388^0.5)= 0.316841
There is some amount of correlation between MOH and DMS
2 Machine hour has better correlation between with MOH
Hence, the Manufacturing overhead will be better parameter for charging Manufacturing Overhead under absorbsion costing
But, the Correlation is still low.
Hence it is recommended that the company use Activity Based Costing system

ekkarill92 answered 1 year ago
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