High-low method, alternative regression functions, accrual accounting adjustments.

Trevor Kennedy, the cost analyst at a can manufacturing plant of United Packaging, is seeking to examine the relationship between total engineering support costs reported in the plant records and machine-hours. These costs have two components: (1) labour (which is paid monthly) and (2) materials and parts (which are purchased from an outside vendor every three months). After further discussion with the operating manager, Kennedy discovers that the materials and parts numbers reported in the monthly records are on an “as purchased” basis and not on an “as used” or accrual accounting basis. By examining materials and parts usage records, Kennedy is able to restate the materials and parts costs to an “as used” basis. (No restatement ofthe labour costs was necessary.) The reported and restated costs are as follows:

Month Labour:

Reported Costs

(1)

Materials and Parts:

Reported Costs

(2)

Materials and Parts:

Restated Costs

(3)

Total Engineering Support:

Reported Costs

(4) = (1) + (2)

Total Engineering Support:

Restated Costs

(5) = (1) + (3)

MachineHours

(6)

March $416 $1,016 $218 $1,432 , $ 634 30 April 625 0 493 625 1,118 63 May 478 0 322 478 800 49 June 426 1,153 274 1,579 700 38 July 568 0 418 568 986 57 August 740 0 419 740 1,159 73 September 294 985 150 1,279 444 19 October 584 0 437 584 1,021 53 November 517 0 348 517 865 42 The regression results, when total engineering support reported costs (column 4) are used as the dependent variable, are Regression 1. Engineering support reported costs = a + (b X machine-hours)

Variable Coefficient Standard Error t-Value Constant $1,671.41 $366.60 4.56 Independent variable 1: machine-hours $ (17.08) $ 7.38 -2.31 r2 = 0.43; Durbin-Watson statistic = 2.48. Adjusted R2 = 0.35 The regression results, when total engineering support restated costs (column 5) are used as the dependent variable, are Regression 2. Engineering support restated costs = a + (b X machine-hours)
Variable Coefficient Standard Error t-Value Constant $211.52 $64.46 3.28 Independent variable 1: machine-hours $13.73 $1.30 10.59 r2 = 0.94; Durbin-Watson statistic = 1.54. Adjusted R2 = 0.933 Instructions Form groups of two or more students to complete the following requirements.
Required 1. Present a plot ofthe data for the cost function relating the reported costs for total engineering support to machine-hours. Present a plot of the data for the cost function relating the restated costs for total engineering support to machine-hours. Comment on the plots.
2. Compute estimates of the cost functions (y — a + bX) for reported engineering support costs and machine-hours and restated engineering support costs and machine-hours using the high-low method.
3. Contrast and evaluate the cost function estimated with regression using restated data for materials and parts with the cost function estimated with regression using the data reported in the plant records. Use the comparison format employed in Exhibit 10-19

(p. 400).

4. Of all the cost functions estimated in requirements 2 and 3, which one would you choose to best represent the relationship between engineering support costs and machine-hours?

Why?

5. Kennedy expects 50 machine-hours to be worked in December. What engineering sup¬

port costs should Kennedy budget for December?

6. What problems might Kennedy encounter when restating the materials and parts costs recorded to an “as used” or accrual accounting basis?

7. Why is it important for Kennedy to pick the correct cost function? That is, illustrate two potential problems Kennedy could run into, by choosing a cost function other than the one you chose in requirement 4.