Exam 5: Cost Estimation

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below: Administrative Credit Month Costs Hours July \ 129,301 250 August 82,613 115 September 225,580 1,392 October 216,394 1,000 November 258,263 1,309 December 184,445 1,112 January 219,137 1,335 February 245,000 1,373 March 209,462 1,064 April 191,925 1,123 May 249,978 1,360 June 170,41\varepsilon 420 July 128,167 315 Total \ 2,510,687 12,172 Average \ 193,130 936 The controller's office has analyzed the data and has given you the results from the regression analysis: SUMMARY OUTPUT Regression Statistics Multiple R 0.9317157 R Square 0.868094147 Adjusted R Square 0.856102705 Standard Error 20,134.92395 Observations 13 ANOVA df S S M S F Significance F Repression 1 29,349,143,514 29,349,143,514 72.3928117 3.61909-06 Residual 11 4,459,566,787 405,415,162.4 Total 12 33,808,710,301 - The correlation coefficient (rounded to the 3^rd decimal) for the regression equation for administrative costs is:

Which of the following is the difference between variable costs and fixed costs? (CMA adapted)

When using past data to predict a cost that has fixed and variable components, it is possible to have an equation with a negative intercept. Does this mean that at a zero production level, the company will make money on its fixed costs? Explain.

Market Products, Inc., has found that new products follow a learning curve. The first two units have been completed with the following results: Units Produced Marginal Labor Time 1 250.00 2 225 Required: (a.) How much time will be needed to complete the 4th unit? (b.) How much time will be needed to complete the 8th unit? (c.) How much time will be needed to complete the 16th unit?

Yates Corp. wants to develop a cost equation for its administrative costs. The controller believes the appropriate cost driver is units produced. Last year's data are presented below: Month Units Produced Administrative Costs January 32,500 \2 4,288 February 37,700 27,720 March 24,700 17,820 April 40,300 30,360 May 49,400 37,290 June 42,900 29,040 July 53,300 32,670 Alugust 45,500 30,030 September 26,000 20,460 October 48,100 34,320 November 61,100 40,920 December 54,600 36,630 SUMMARY OUTPUT Regrerrian Statirticr Multiple R 0.96538 R Squjare 0.931965 Adjusted R-Square 0.925162 Starddard Error 1,881.23 Observations 12 Coefficiend Standard Error t Stat P-valke Lower 95\% Upper 95\% Irtercept 4,919.48 2,221.329 2.21 0.051156 -29.9485 9,868.909 Units produced 0.586154 0.050082 11.70 3.69-07 0.474560 0.697743 Required: (a.) What is the equation for administrative costs using the regression analysis? (b.) Does the variable "units produced" have statistical significance? Explain. (c.) Prepare an estimate of administrative costs for a month when 30,000 units are produced.

Identifying the relation between the activity and the costs is a key step in which of the following cost estimation methods?

Which of the following statements is(are) true regarding cost behaviors? (A) In general, accounting records accumulate cost information according to its behavior. (B) Cost behaviors are the most important consideration in managerial decision making.

What are "outliers" and what effect does their presence have when using regression analysis for cost estimation?

The McGraw Company is accumulating data to be used in preparing its annual profit plan for the coming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff has suggested that linear regression be employed to derive an equation in the form of y = a + bx for maintenance costs. Data regarding the maintenance hours and costs for last year and the results of the regression analysis are as follows: (CMA adapted) Hours of Activity Maintenance Costs January 480 \ 4,200 February 320 3,000 March 400 3,600 April 300 2,820 May 500 4,350 June 310 2,960 July 320 3,030 August 520 4,470 September 490 4,260 October 470 4,050 November 350 3,300 December 340 3,160 Sum 4,800 \ 3,200 Average 400 3,600 A coefficient 684.65 B coefficient 7.2884 Standard error of the a coefficient 49.515 Standard error of the b coefficient 0.12126 Standard error of the estimate 34.469 0.99724 T-value a 13.827 T-value b 60.105 - What would be the cost equation if the high-low method is used?

Which of the following is not a data problem an analyst must watch for when estimating cost behavior?

The McGraw Company is accumulating data to be used in preparing its annual profit plan for the coming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff has suggested that linear regression be employed to derive an equation in the form of y = a + bx for maintenance costs. Data regarding the maintenance hours and costs for last year and the results of the regression analysis are as follows: (CMA adapted) Hours of Activity Maintenance Costs January 480 \ 4,200 February 320 3,000 March 400 3,600 April 300 2,820 May 500 4,350 June 310 2,960 July 320 3,030 August 520 4,470 September 490 4,260 October 470 4,050 November 350 3,300 December 340 3,160 Sum 4,800 \ 3,200 Average 400 3,600 A coefficient 684.65 B coefficient 7.2884 Standard error of the a coefficient 49.515 Standard error of the b coefficient 0.12126 Standard error of the estimate 34.469 0.99724 T-value a 13.827 T-value b 60.105 -What would be the cost equation if regression analysis is used?

The Norcross Company has traditionally estimated manufacturing overhead costs using production volume. Some of the production managers believe that the number of setups may also have an impact on monthly manufacturing overhead costs. In order to investigate this possibility, the company collected information on its monthly manufacturing overhead costs, production in units, and number of setups for 2020. Month Production (Units) Manufacturing Overhead Cost Number of Setups 1 50,000 \ 800,100 17 2 65,000 752,500 16 3 40,000 795,100 21 4 55,000 822,750 16 5 30,000 771,225 21 6 25,000 706,200 15 7 60,000 843,000 19 8 80,000 935,200 22 9 77,000 901,750 20 10 43,000 786,400 17 11 50,000 819,600 19 12 70,000 880,900 21 Regression analysis results of the information presented above are as follows: Ordinary regression: Equation: $650,398 + $3.1061 × units r-square: .707 Multiple regression: Equation: [$464,481 + ($2.5356 × units)] + [($11,631.6048 × number of set ups)] r-square: .867 Required: (a.) Use the results from the ordinary regression and estimate next month's manufacturing overhead costs, assuming the company is planning to produce 75,000 units. (final answer should be rounded to the nearest whole dollar) (b.) Use the results from the multiple regression and estimate the next month's manufacturing overhead costs, assuming the company is planning to produce 75,000 units with 18 set ups. (final answer should be rounded to the nearest whole dollar) (c.) Comment on which regression seems to be more appropriate under these circumstances. What additional information would you like to see? Be specific.

Argo Company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable. The results are summarized below: Intercept \ 14,600 Slope \ 12.55 Correlation coefficient 0.931 R-squared 0.867 Argo is planning on operating at a level that would require 12,000 direct labor hours per month in the upcoming year. Required: (a.) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs. (b.) Compute the estimated manufacturing overhead costs per month for the upcoming year.

One advantage that regression techniques have over other cost estimation methods is it generates information that can be used to determine how well the estimated cost equation will predict future costs.

The McGraw Company is accumulating data to be used in preparing its annual profit plan for the coming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff has suggested that linear regression be employed to derive an equation in the form of y = a + bx for maintenance costs. Data regarding the maintenance hours and costs for last year and the results of the regression analysis are as follows: (CMA adapted) Hours of Activity Maintenance Costs January 480 \ 4,200 February 320 3,000 March 400 3,600 April 300 2,820 May 500 4,350 June 310 2,960 July 320 3,030 August 520 4,470 September 490 4,260 October 470 4,050 November 350 3,300 December 340 3,160 Sum 4,800 \ 3,200 Average 400 3,600 A coefficient 684.65 B coefficient 7.2884 Standard error of the a coefficient 49.515 Standard error of the b coefficient 0.12126 Standard error of the estimate 34.469 0.99724 T-value a 13.827 T-value b 60.105 -What is the variable cost per hour using the high-low method to estimate the cost equation?

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below: Administrative Credit Month Costs Hours July \ 129,301 250 August 82,613 115 September 225,580 1,392 October 216,394 1,000 November 258,263 1,309 December 184,445 1,112 January 219,137 1,335 February 245,000 1,373 March 209,462 1,064 April 191,925 1,123 May 249,978 1,360 June 170,41\varepsilon 420 July 128,167 315 Total \ 2,510,687 12,172 Average \ 193,130 936 The controller's office has analyzed the data and has given you the results from the regression analysis: SUMMARY OUTPUT Regression Statistics Multiple R 0.9317157 R Square 0.868094147 Adjusted R Square 0.856102705 Standard Error 20,134.92395 Observations 13 ANOVA df S S M S F Significance F Repression 1 29,349,143,514 29,349,143,514 72.3928117 3.61909-06 Residual 11 4,459,566,787 405,415,162.4 Total 12 33,808,710,301 - If the controller uses the high-low method to estimate costs, the cost equation for administrative costs is

Yates Corp. wants to develop a cost equation for its administrative costs. The controller believes the appropriate cost driver is units produced. Last year's data are presented below: Month Units Produced Administrative Costs January 32,500 \2 4,288 February 37,700 27,720 March 24,700 17,820 April 40,300 30,360 May 49,400 37,290 June 42,900 29,040 July 53,300 32,670 Alugust 45,500 30,030 September 26,000 20,460 October 48,100 34,320 November 61,100 40,920 December 54,600 36,630 Total 516,100 \3 61,548 Average 43,008 \3 0,129 Required: (a.) What is the equation for administrative costs using the high-low method? (b.) Prepare an estimate of administrative costs for a month when 30,000 units are produced.

The Crater Manufacturing Company recorded overhead costs of $14,182 at an activity level of 4,200 machine hours and $8,748 at 2,300 machine hours. The records also indicated that overhead of $9,730 was incurred at 2,600 machine hours. What is the variable cost per machine hour using the high-low method to estimate the cost equation?

In the learning curve equation Y = aX^b, the "b" term represents:

The McGraw Company is accumulating data to be used in preparing its annual profit plan for the coming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff has suggested that linear regression be employed to derive an equation in the form of y = a + bx for maintenance costs. Data regarding the maintenance hours and costs for last year and the results of the regression analysis are as follows: (CMA adapted) Hours of Activity Maintenance Costs January 480 \ 4,200 February 320 3,000 March 400 3,600 April 300 2,820 May 500 4,350 June 310 2,960 July 320 3,030 August 520 4,470 September 490 4,260 October 470 4,050 November 350 3,300 December 340 3,160 Sum 4,800 \ 3,200 Average 400 3,600 A coefficient 684.65 B coefficient 7.2884 Standard error of the a coefficient 49.515 Standard error of the b coefficient 0.12126 Standard error of the estimate 34.469 0.99724 T-value a 13.827 T-value b 60.105 -Based upon the data derived from the regression analysis, 420 maintenance hours in a month would mean the maintenance costs would be budgeted at: (rounded to the nearest whole dollar)