Exam 5: Cost Estimation

Clough Company is interested in establishing the relationship between utility costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow: Utility Month Machine Hours Costs January 3,250 \ 2,080 February 3,770 25,200 March 2,470 16,200 April 4,030 27,600 May 4,940 33,900 June 4,290 26,400 July 5,330 29,700 August 4,550 27,300 September 2,600 18,600 October 4,810 31,200 November 6,110 37,200 December 5,460 33,300 Required: (a.) What is the equation for utility costs using the high-low method? (b.) Prepare an estimate of utility costs for a month when 3,000 machine hours are worked.

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow: Month Machine Hours Electricity Costs January 2,500 \ 18,400 February 2,900 21,000 March 1,900 13,500 April 3,100 23,000 May 3,800 28,250 June 3,300 22,000 July 4,100 24,750 August 3,500 22,750 September 2,000 15,500 October 3,700 26,000 November 4,700 31,000 December 4,200 27,750 Summary Output Regression Statistics Multiple R 0.965 R Squuare 0.932 0.925 Standard Error 1,425.18 Observations 12.00 Standard Lower Upper Coefficients Error t Stat P-value 95\% 95\% Intercept 3,726.88 1,682.82 2.21 0.05 (22.69) 7,476.45 Machine 5.77 0.49 11.7 0.00 4.67 6.87 Hours - If the controller uses regression analysis to estimate costs, the estimate of the variable portion of electricity costs is:

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 - Based on the results of the high-low analysis, the estimate of administrative costs in a month with 1,000 credit hours would be: (rounded to the nearest whole dollar)

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow: Month Machine Hours Electricity Costs January 2,500 \ 18,400 February 2,900 21,000 March 1,900 13,500 April 3,100 23,000 May 3,800 28,250 June 3,300 22,000 July 4,100 24,750 August 3,500 22,750 September 2,000 15,500 October 3,700 26,000 November 4,700 31,000 December 4,200 27,750 Summary Output Regression Statistics Multiple R 0.965 R Squuare 0.932 0.925 Standard Error 1,425.18 Observations 12.00 Standard Lower Upper Coefficients Error t Stat P-value 95\% 95\% Intercept 3,726.88 1,682.82 2.21 0.05 (22.69) 7,476.45 Machine 5.77 0.49 11.7 0.00 4.67 6.87 Hours - Based on the results of the high-low analysis, the estimate of electricity costs in a month with 2,200 machine hours would be:

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 percent of the total variance that can be explained by the regression is:

The relevant range represents those activity levels for which valid cost relationships have been observed.

The Feline Company has been having some difficulties estimating its manufacturing overhead costs. In the past, manufacturing overhead costs have been related to production levels. However, some production managers have indicated that the size of their production lots might also be having an impact on the amount of their monthly manufacturing overhead costs. In order to investigate this possibility, the company collected information on its monthly manufacturing overhead costs, production in units, and average production lot size for 2020. Average Monthly Production Manufacturing Production Month (Units) Overhead Cost Lot Size 1 75,000 \ 25,800 20 2 90,000 843,875 19 3 65,000 910,125 24 4 80,000 946,000 19 5 55,000 879,000 24 6 50,000 825,000 18 7 85,000 960,000 22 8 105,000 1,053,500 25 9 102,000 1,020,000 23 10 68,000 905,000 20 11 75,000 938,000 22 12 95,000 995,000 24 Required: (a.) Use the high-low method to estimate next month's manufacturing overhead costs, assuming the company is planning to produce 92,000 units. (b.) Use the high-low method to estimate next month's manufacturing overhead costs, assuming the company is planning to run a 21-lot size.

Brewsky's is a chain of micro-breweries. Managers are interested in the costs of the stores and believe that the costs can be explained in large part by the number of customers patronizing the stores. Monthly data regarding customer visits and costs for the preceding year for one of the stores have been entered into the regression analysis and the analysis is as follows: Average monthly customer visits 1,462 Average monthly total costs \4 ,629 Regression Results Intercept \1 ,496 b coefficient \2 .08 0.86814 - What is the percent of the total variance that can be explained by the regression equation? (CMA adapted)

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow: Month Machine Hours Electricity Costs January 2,500 \ 18,400 February 2,900 21,000 March 1,900 13,500 April 3,100 23,000 May 3,800 28,250 June 3,300 22,000 July 4,100 24,750 August 3,500 22,750 September 2,000 15,500 October 3,700 26,000 November 4,700 31,000 December 4,200 27,750 Summary Output Regression Statistic Multiple R 0.965 R Squuare 0.932 0.925 Standard Error 1,425.18 Observations 12.00 Standard Lower Upper Coefficients Error t Stat P-value 95\% 95\% Intercept 3,726.88 1,682.82 2.21 0.05 (22.69) 7,476.45 Machine 5.77 0.49 11.7 0.00 4.67 6.87 Hours - The correlation coefficient for the regression equation for electricity costs is:

The Macon Company uses the high-low method to determine its cost equation. The following information was gathered for the past year: Machine Direct Labor Hours Costs Busiest month (June) 14,000 \ 200,000 Slowest month (December) 6,000 \ 120,000 - If Macon expects to use 10,000 machine hours next month, what are the estimated direct labor costs?

Balcom Enterprises is planning to introduce a new product that will sell for $110 per unit. Manufacturing cost estimates for 20,000 units for the first year of production are: ? Direct materials $1,000,000. ? Direct labor $720,000 (based on $18 per hour × 40,000 hours). Although overhead has not be estimated for the new product, monthly data for Balcom's total production for the last two years has been analyzed using simple linear regression. The analysis results are as follows: Dependent variable Factory overhead costs Independent variable Direct labor hours Intercept \ 120,000 Coefficient on independent variable \ 5.00 Coefficient of correlation 0.911 0.814 - Based on this information, how much is the variable manufacturing cost per unit, using the variable overhead estimated by the regression (assuming that direct materials and direct labor are variable costs)?

In the cost equation TC = F + VX, "X" is best described as the:

Different cost estimations methods may produce different cost equations, even when using the same set of data.

Balcom Enterprises is planning to introduce a new product that will sell for $110 per unit. Manufacturing cost estimates for 20,000 units for the first year of production are: ? Direct materials $1,000,000. ? Direct labor $720,000 (based on $18 per hour × 40,000 hours). Although overhead has not be estimated for the new product, monthly data for Balcom's total production for the last two years has been analyzed using simple linear regression. The analysis results are as follows: Dependent variable Factory overhead costs Independent variable Direct labor hours Intercept \ 120,000 Coefficient on independent variable \ 5.00 Coefficient of correlation 0.911 0.814 - Based on this information, what percentage of the variation in overhead costs is explained by the independent variable?

Below are several examples of costs that are labeled fixed or variable according to their typical accounting designations. Under which circumstances would any of these costs behave in a manner opposite to that listed? a. Direct labor-variable. b. Equipment depreciation-fixed. c. Utilities (with a minimum charge)-variable. d. Supervisory salaries-fixed. e. Indirect materials purchased in given lot sizes that become spoiled within a few days-variable.

A scattergraph is useful for identifying outliers/irrelevant data points.

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow: Month Machine Hours Electricity Costs January 2,500 \ 18,400 February 2,900 21,000 March 1,900 13,500 April 3,100 23,000 May 3,800 28,250 June 3,300 22,000 July 4,100 24,750 August 3,500 22,750 September 2,000 15,500 October 3,700 26,000 November 4,700 31,000 December 4,200 27,750 Summary Output Regression Statistics Multiple R 0.965 R Squuare 0.932 0.925 Standard Error 1,425.18 Observations 12.00 Standard Lower Upper Coefficients Error t Stat P-value 95\% 95\% Intercept 3,726.88 1,682.82 2.21 0.05 (22.69) 7,476.45 Machine 5.77 0.49 11.7 0.00 4.67 6.87 Hours - Based on the results of the regression analysis, the estimate of electricity costs in a month with 2,200 machine hours would be: (rounded to the nearest whole dollar)

New Venture, Inc. has received a contract for 8 units of a new product. The contract is a cost-plus contract, with the total to be received equal to the total labor cost + 20%. New Venture found that the first unit of a new product required 120 hours to complete. The second unit was completed using only 114 hours. New Venture believes that the rate of learning that was observed will continue for all 8 units of the contract. The labor wage paid is $25/hour. The following factors are available for various rates of learning: 80% learning, b = -0.3219; 85%, b = -0.2345; 90%, b = -0.1520; 95%, b = -0.0740. Required: (a.) What will the total labor cost be for the contract? (b.) What will the total fee be for the contract?

J.C. Riley, who owns Riley's Auto Repair Shop is trying to determine whether the company's advertising program is successful. He has used a spreadsheet program to estimate the relationship between advertising expenditures and sales dollars. Monthly data for the past two years were entered into the program. The regression results indicated the following: Sales dollars = $169, 000 − ($200 × Advertising expenditures) Correlation coefficient = −0.864 To J.C., the results imply that advertising is actually reducing sales. Can you help explain to him what might cause the negative relationship between advertising expenditures and sales?

A disadvantage of the high-low method of cost analysis is that it: