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Homework – Problem #2 2. Ethics: Manipulating Data to Establish a Budget. Healthy Bar, Inc., produces...

Homework – Problem #2

2. Ethics: Manipulating Data to Establish a Budget.

Healthy Bar, Inc., produces energy bars for sports enthusiasts. The company’s fiscal year ends on December 31. The production manager, Jim Wallace, is establishing a cost budget for the production department for each month of this coming quarter (January through March). At the end of March, Jim will be evaluated based on his ability to meet the budget for the three months ending March 31. In fact, Jim will receive a significant bonus if actual costs are below budgeted costs for the quarter. The production budget is typically established based on data from the last 18 months. These data are as follows:

Reporting Period (Month)

Total Overhead Costs

Total Machine Hours

July

$686,000

3,390

August

700,000

3,441

September

652,000

2,443

October

715,000

3,730

November

645,000

2,433

December

665,700

2,680

January

676,500

2,935

February

701,000

3,641

March

614,000

2,202

April

627,000

2,231

May

642,000

2,242

June

692,000

3,498

July

711,000

3,585

August

725,000

2,541

September

691,200

3,893

October

757,600

2,893

November

670,900

2,522

December

701,100

2,699

You are the accountant who assists Jim in preparing an estimate of production costs for the next three months. You intend to use regression analysis to estimate costs, as was done in the past. Jim expects that 3,100 machine hours will be used in January, 3,650 machine hours in February, and 2,850 machine hours in March.

Jim approaches you and asks that you add $100,000 to production costs for each of the past 18 months before running the regression analysis. As he puts it, “After all, management always takes my proposed budgets and reduces them by about 10 percent. This is my way of leveling the playing field!”

Required:

A. Use Excel to perform regression analysis using the historical data provided.

  1. Submit a printout of the results. Explain.
  2. Use the regression output to develop the cost equation Y = f + vX by filling in the dollar amounts for f and v. Explain procedure and results in detail.
  3. Calculate estimated production costs for January, February, and March. Also provide a total for three months. Explain procedure and results in detail.

B. Use Excel to perform regression analysis after adding $100,000 to production costs for each of the past 18 months, as Jim requested.

  1. Submit a printout of the results. Explain procedure and results in detail and compare with #
  2. Use the regression output to develop the cost equation Y = f + vX by filling in the dollar amounts for f and v. Explain procedure and results in detail and compare with #1
  3. Calculate estimated production costs for January, February, and March. Also provide a total for the three months. Explain procedure and results in detail and Compare with #1

C. Why did Jim ask you to add $100,000 to production costs for each of the past 18 months? Explain

D. How should you handle Jim’s request? Explain

Solutions

Expert Solution

A)

Reporting Period (Month) Total Overhead Costs (y) Total Machine Hours (x) xy($) x^2 y^2
July $686,000 3,390 2,325,540,000.00 11,492,100 $470,596,000,000
August 700,000 3,441 2,408,700,000.00 11,840,481 $490,000,000,000
September 652,000 2,443 1,592,836,000.00 5,968,249 $425,104,000,000
October 715,000 3,730 2,666,950,000.00 13,912,900 $511,225,000,000
November 645,000 2,433 1,569,285,000.00 5,919,489 $416,025,000,000
December 665,700 2,680 1,784,076,000.00 7,182,400 $443,156,490,000
January 676,500 2,935 1,985,527,500.00 8,614,225 $457,652,250,000
February 701,000 3,641 2,552,341,000.00 13,256,881 $491,401,000,000
March 614,000 2,202 1,352,028,000.00 4,848,804 $376,996,000,000
April 627,000 2,231 1,398,837,000.00 4,977,361 $393,129,000,000
May 642,000 2,242 1,439,364,000.00 5,026,564 $412,164,000,000
June 692,000 3,498 2,420,616,000.00 12,236,004 $478,864,000,000
July 711,000 3,585 2,548,935,000.00 12,852,225 $505,521,000,000
August 725,000 2,541 1,842,225,000.00 6,456,681 $525,625,000,000
September 691,200 3,893 2,690,841,600.00 15,155,449 $477,757,440,000
October 757,600 2,893 2,191,736,800.00 8,369,449 $573,957,760,000
November 670,900 2,522 1,692,009,800.00 6,360,484 $450,106,810,000
December 701,100 2,699 1,892,268,900.00 7,284,601 $491,541,210,000
SUM(Ʃ) 12,273,000 52,999 36,354,117,600 161,754,347 8,390,821,960,000
y x xy($) x^2 y^2
569,505.30
38.15
Linear equation y=f+vx Unit Variable Cost (v) = (Σy)(Σx2) − (Σxy) 569,505.30
y Total overhead cost nΣx2 − (Σx)2
f Fixed cost' Total Fixed Cost (f) = Σxy − (Σx)(Σy)
v variable cost nΣx2 − (Σx)2 38.15
x hours
y= 569505.3+38.15x
Using the above equation-
Reporting Period (Month) Total Overhead Costs (y) Total Machine Hours (x)
Jan 687770.30 3,100.00 569595.30+38.15(3100)
Feb' 708752.80 3,650.00 569595.30+38.15(3650)
Mar' 678232.80 2,850.00 569595.30+38.15(2850)   

B) Similarly if $100000 is added

Reporting Period (Month) Total Overhead Costs (y) Total Machine Hours (x) xy($) x^2 y^2
July $786,000 3,390 2,664,540,000.00 11,492,100 $617,796,000,000
August 800,000 3,441 2,752,800,000.00 11,840,481 $640,000,000,000
September 752,000 2,443 1,837,136,000.00 5,968,249 $565,504,000,000
October 815,000 3,730 3,039,950,000.00 13,912,900 $664,225,000,000
November 745,000 2,433 1,812,585,000.00 5,919,489 $555,025,000,000
December 765,700 2,680 2,052,076,000.00 7,182,400 $586,296,490,000
January 776,500 2,935 2,279,027,500.00 8,614,225 $602,952,250,000
February 801,000 3,641 2,916,441,000.00 13,256,881 $641,601,000,000
March 714,000 2,202 1,572,228,000.00 4,848,804 $509,796,000,000
April 727,000 2,231 1,621,937,000.00 4,977,361 $528,529,000,000
May 742,000 2,242 1,663,564,000.00 5,026,564 $550,564,000,000
June 792,000 3,498 2,770,416,000.00 12,236,004 $627,264,000,000
July 811,000 3,585 2,907,435,000.00 12,852,225 $657,721,000,000
August 825,000 2,541 2,096,325,000.00 6,456,681 $680,625,000,000
September 791,200 3,893 3,080,141,600.00 15,155,449 $625,997,440,000
October 857,600 2,893 2,481,036,800.00 8,369,449 $735,477,760,000
November 770,900

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