Question

Q1. Eagle plc Eagle plc, the Australian subsidiary of a US packaging company, is preparing its budget for the year to 30th June 20X0. In respect of fuel oil consumption, it is desired to estimate an equation of the form y = a + bx, where y is the total expense at an activity level x, a is the fixed expense and b is the rate of variable cost. The following data relate to the year to 30th June 20XO: Month 34 July 19X8 August September October November December January 1989 February March April Machine hours Fuel oil expense (000s) ($) 640 30 620 34 620 590 42 500 530 500 500 31 530 550 580 48 680 26 26 35 May 43 June The annual total and monthly average figures for 20X0 were as follows: ($) Machine hours Fuel oil expense (000s) 420 6840 570 Annual total Monthly Average 35 You are required to: (a) Estimate fixed and variable elements of fuel oil expense from the above data by both of the following methods: High and low points; Least-squares regression analysis (ii) (b) Compare briefly the methods used in (a) above in relation to the task of estimating fixed and variable elements of a semi-variable cost.

Answer 1

High and Low Points High Low Method Difference in Total Fuel Oil expense Difference In Machine Hours High level of activity Low level of activity Change in activity Number of Machine Hours (A) Total Fuel Oil expense(B) 48 680 26 500 22 180 Variable Cost (BIA) 8.18 Per Machine Hour Determine the fixed cost component using the high-low method Number of Machine Hours Total Fuel Oil expense Less Variable component (48*8.18) Fixed component 48 680 393 287 26 500 213 (26*8.18) 287 Total expense = 287 + 8.18 * Number of Machine Hours

Least Square Regression Analysis Month Machine Hours Fuel Oil Expense 640 620 July August September October November December January February March April May June 620 590 500 530 500 500 530 550 580 680 26 43 48 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.496661842 0.246672986 0.171340284 55.4398252 12

ANOVA Regression Residual Total SS MS F gnificance F 10064.25781 10064.26 3.274448 0.100477 30735.74219 3073.574 40800 11 Intercept Machine Hours Coefficients 414.8242188 4.43359375 Standard Error tStat P-value Lower 95%Upper 95%ower 95.09pper 95.0% 87.23472718 4.755265 0.000774 220.4531 609.1953 220.4531 609.1953 2.450117272 1.809543 0.100477 -1.02561 9.892795 -1.02561 9.892795 RESIDUAL OUTPUT Observation 108 109 110 111 112 113 114 115 116 117 Predicted Fuel Oil Expense 565.5664063 547.8320313 565.5664063 587.734375 601.0351563 556.6992188 530.0976563 530.0976563 552.265625 Residuals 74.43359375 72.16796875 54.43359375 2.265625 -101.0351563 -26.69921875 -30.09765625 -30.09765625 -22.265625 -20 -25.46875 52.36328125 570 605.46875 627.6367188

Total expense = 414.82 +4.43 * Number of Machine Hours 119 120 121 b) 122 123 Least Square method provides better estimate of Fixed Cost and Variable Cost Per Unit because it takes into account the entire figures whereas High Low method takes into account only the two figures i.e highest and lowest, therefore the estimate of fixed cost and Variable cost do not represent the entire data. 124 125 126