1. © 2014 by McGraw-Hill Education. This is proprietary material solely for authorized instructor use. Not authorized for sale or distribution in any manner. This document may not be copied, scanned, duplicated, forwarded, distributed, or posted on a website, in whole or part.

2. PowerPoint Authors: Susan Coomer Galbreath, Ph.D., CPA Charles W. Caldwell, D.B.A., CMA Jon A. Booker, Ph.D., CPA, CIA Cynthia J. Rooney, Ph.D., CPA Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Cost Estimation Chapter 5

3. 5-3 Learning Objectives LO 5-1 Understand the reasons for estimating fixed and variable costs. LO 5-2 Estimate costs using engineering estimates. LO 5-3 Estimate costs using account analysis. LO 5-4 Estimate costs using statistical analysis. LO 5-5 Interpret the results of regression output. LO 5-6 Identify potential problems with regression data. LO 5-7 Evaluate the advantages and disadvantages of alternative cost estimation methods. LO 5-8 (Appendix A) Use Microsoft Excel to perform a regression analysis. LO 5-9 (Appendix B) Understand the mathematical relationship describing the learning phenomenon.

4. 5-4 Why Estimate Costs? Managers make decisions and need to compare costs and benefits among alternative actions. Cost estimates can be an important element In helping managers make decisions.

5. 5-5 Basic Cost Behavior Patterns LO 5-1 Understand the reasons for estimating fixed and variable costs. Costs Fixed costsVariable costs Total fixed costs do not change proportionately as activity changes. Per unit fixed costs change inversely as activity changes. Total variable costs change proportionately as activity changes. Per unit variable cost remain constant as activity changes. LO 5-1

6. 5-6 Methods Used to Estimate Cost Behavior LO 5-1 Charlene, owner of Charlene’s Computer Care (3C), wants to estimate the cost of a new computer repair center. 1.Engineering estimates 2.Account analysis 3.Statistical methods

7. 5-7 Engineering Estimates LO 5-2 Estimate costs using engineering estimates. Cost estimates are based on measuring and then pricing the work involved in a task. Identify the activities involved: – Labor – Rent – Insurance Estimate the time and cost for each activity. LO 5-2

8. 5-8 Engineering Estimates Details each step required to perform an operation Permits comparison of other centers with similar operations Costs for totally new activities can be estimated without prior activity data. Can be quite expensive to use Based on optimal conditions LO 5-2

9. 5-9 Account Analysis LO 5-3 Estimate costs using account analysis. Review each account comprising the total cost being analyzed. Identify each cost as either fixed or variable. FixedVariable LO 5-3

10. 5-10 Account Analysis Costs for 360 repair-hours Office rent Utilities Administration Supplies Training Other Total Per repair hour $ 3,375 310 3,386 2,276 666 613 $10,626 $1,375 100 186 2,176 316 257 $4,410 $12.25 $2,000 210 3,200 100 350 356 $6,216 AccountTotal Variable cost Fixed cost LO 5-3

11. 5-11 Account Analysis Fixed costs + (Variable cost/unit × No. of units) = Total cost Cost at 360 repair-hours: $6,216 + ($12.25 × 360) = $10,626 Cost at 480 repair-hours: $6,216 + ($12.25 × 480) = $12,096 LO 5-3

12. 5-12 Account Analysis Managers and accountants are familiar with company operations and the way costs react to changes in activity levels. Managers and accountants may be biased. Decisions often have major economic consequences for managers and accountants. LO 5-3

13. 5-13 Statistical Cost Estimation LO 5-4 Estimate costs using statistical analysis. Analyze costs within a relevant range, which is the limits within which a cost estimate may be valid. Relevant range for a projection is usually between the upper and lower limits (bounds) of past activity levels for which data is available. LO 5-4

14. 5-14 Overhead Cost Estimation for 3C 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 $ 9,891 $ 9,244 $13,200 $10,555 $ 9,054 $10,662 $12,883 $10,345 $11,217 $13,269 $10,830 $12,607 $10,871 $12,816 $ 8,464 248 480 284 200 380 568 344 448 544 340 412 384 404 212 Month Overhead costs Repair- hours These data will be used to estimate costs using a statistical analysis. LO 5-4

15. 5-15 Scattergraph Does it look like a relationship exists between repair-hours and overhead costs? LO 5-4

16. 5-16 Scattergraph We use “eyeball judgment” to determine the intercept and slope of the line. LO 5-4

17. 5-17 Hi-Low Cost Estimation This is a method to estimate cost based on two cost observations, the highest and lowest activity level. High Low Change $12,883 $ 9,054 $ 3,829 568 200 368 Month Overhead costs Repair- hours LO 5-4

18. 5-18 Hi-Low Cost Estimation Fixed cost (F) = Total cost at highest activity –(Variable cost × Highest activity level) Total cost at lowest activity –(Variable cost × Lowest activity level) Variable cost per unit (V) = (Cost at highest activity level – Cost at lowest activity level) (Highest activity level – Lowest activity level) LO 5-4

19. 5-19 Hi-Low Cost Estimation Variable cost per RH (V) = ($12,883 – $9,054) 568 RH – 200 RH = $3,829 368 RH = $10.40 per RH Fixed costs (F)=($12,883 – ($10.40 × 568 RH)=$6,976 Rounding difference LO 5-4 Fixed costs (F) =($9,054 – ($10.40 × 200 RH)=$6,974

20. 5-20 Hi-Low Cost Estimation How do we estimate manufacturing overhead with 480 repair-hours? TC = F + VX TC = $6,976 + ($10.40 × 480) = $11,968 LO 5-4

21. 5-21 Regression Analysis Regression is a statistical procedure to determine the relation between variables. It helps managers determine how well the estimated regression equation describes the relations between costs and activities. LO 5-4

22. 5-22 Regression Analysis Hi-low method: Uses two data points Regression: Uses all of the data points LO 5-4

23. 5-23 Regression Analysis Y = a + bX Y = Intercept + (Slope × X) For 3C: OH = Fixed costs + (V × Repair-hours) LO 5-4

24. 5-24 Interpreting Regression LO 5-5 Interpret the results of regression output. Independent variable: –The X term, or predictor –The activity that predicts (causes) the change in costs Activities: –Repair-hours Dependent variable: –The Y term –The dependent variable –The cost to be estimated Costs: –Overhead costs LO 5-5

25. 5-25 Interpreting Regression The computer output of 3C’s scattergraph gives the following formula: Total overhead = $6,472 + ($12.52 per RH × No. of RH) Estimate 3C’s overhead with 480 repair hours. TC = F + VX TC = $6,472 + ($12.52 × 480) = $12,482 LO 5-5

26. 5-26 Interpreting Regression Correlation coefficient (R): This measures the linear relationship between variables.The closer R is to 1.0 the closer the points are to the regression line. The closer R is to zero, the poorer the fit of the regression line. Coefficient of determination (R 2 ): This is the square of the correlation coefficient. It is the proportion of the variation in the dependent variable (Y) explained by the independent variable(s) (X). LO 5-5

27. 5-27 Interpreting Regression T-statistic: This is the value of the estimated coefficient, b, divided by its estimated standard error (Se b ). Generally, if it is over 2, then it is considered significant. If significant, the cost is NOT totally fixed. From the data used in the 3C regression, the t-statistic is: t= b ÷ Se b = 12.5230 ÷ 1.5843 = 7.9044 LO 5-5

28. 5-28 Interpreting Regression An 0.91 correlation coefficient means that a linear relationship does exists between repair hours and overhead costs. An 0.828 coefficient of determination means that 82.8% of the changes in overhead costs can be explained by changes in repair-hours. Both have t-statistics that are greater than 2, so the cost is not totally fixed. LO 5-5

29. 5-29 Multiple Regression Multiple regression: When more than one predictor (x) is in the model Is repair-hours the only activity that drives overhead costs at 3C? Predictors: X 1 : Repair-hours X 2 : Parts cost Equation: TC = VC(X 1 ) + VC(X 2 ) + FC LO 5-5

30. 5-30 Multiple Regression Output The adjusted R-squared is the correlation coefficient squared and adjusted for the number of independent variables used to make the estimate. The statistics supplied with the output (rounded off) are: – Correlation coefficient (R) = 0.953 – R 2 = 0.908 – Adjusted R 2 = 0.892 LO 5-5

31. 5-31 Multiple Regression Output TC = F + V 1 X 1 + V 2 X 2 TC = $6,416 + ($8.61 × 480) + (77% × $3,500) TC = $13,244 LO 5-5

32. 5-32 LO 5-6 Identify potential problems with regression data. Effect of: –Nonlinear relations –Outliers –Spurious relations –Using data that do not fit the assumptions of regression analysis LO 5-6 Practical Implementation Problems

33. 5-33 Volume Cost 0 5 10 15 20 25 30 35 40 $800 $700 $600 $500 $400 $300 $200 $100 $0 Assumed actual cost function Relevant range Regression estimate Capacity The Effect of Nonlinear Relations LO 5-6 Practical Implementation Problems

34. 5-34 Problem: Attempting to fit a linear model to nonlinear data Likely to occur near full-capacity Solution: Define a more limited relevant range. (Example: from 25-75% capacity) Design a nonlinear model. LO 5-6 Practical Implementation Problems

35. 5-35 Computed regression line True regression line “Outlier” The Effect of Outliers on the Computed Regression LO 5-6 Practical Implementation Problems

36. 5-36 Problem: Outliers move the regression line. Solution: Prepare a scattergraph, analyze the graph, and eliminate highly unusual observations before running the regression. LO 5-6 Practical Implementation Problems

37. 5-37 The Effect of Spurious Relations Problem: Using too many variables in the regression (i.e., using direct labor to explain materials costs). Although the association is very high, actually both are driven by output. Solution: Carefully analyze each variable and determine the relationship among all elements before using in the regression. LO 5-6 Practical Implementation Problems

38. 5-38 The Effect of Using Data That Do Not Fit the Assumptions of Regression Problem: If the assumptions in the regression are not satisfied, then the regression is not reliable. Solution: There is no clear solution. Limit time to help assure costs behavior remains constant, yet this causes the model to be weaker due to less data. LO 5-6 Practical Implementation Problems

39. 5-39 Learning Phenomenon Learning phenomenon is the systematic relationship between the amount of experience in performing a task and the time required to perform it. LO 5-6

40. 5-40 How an Estimation Method is Chosen LO 5-7 Evaluate the advantages and disadvantages of alternative cost estimation methods. Reliance on historical data is relatively inexpensive. Computational tools allow for more data to be used than for non-statistical methods. Reliance on historical data may be the only readily available, cost-effective basis for estimating costs. Analysts must be alert to cost-activity changes. LO 5-7

41. 5-41 Data Problems Missing data Outliers Allocated and discretionary costs Inflation Mismatched time periods LO 5-7

42. 5-42 Effect of Different Methods on Cost Estimates Account analysis High-low Simple regression Multiple regression $12,096 $11,968 $12,482 $13,244 $6,216 $6,976 $6,472 $6,416 $12.25/repair-hour $10.40/repair-hour $12.52/repair-hour $8.61/repair-hour + 77% of parts cost Method Total estimated costs Estimated fixed costs Estimated variable costs Estimated manufacturing overhead with 480 repair-hours. LO 5-7

43. 5-43 Appendix A: Regression Analysis Using Microsoft Excel LO 5-8 (Appendix A) Use Microsoft Excel to perform a regression analysis. Many software programs exist to aid in performing regression analysis. Data is entered and the user then selects the data and type of regression analysis to be generated. The analyst must be well schooled in regression in order to determine the meaning of the output. In order to use Microsoft Excel, the Analysis Tool Pak must be installed. LO 5-8

44. 5-44 Appendix B: Learning Curves This is the systematic relationship between the amount of experience in performing a task and the time required to perform it. First unit Second unit Fourth unit Eighth unit 100.0 hours 80.0 hours 64.0 hours 51.2 hours (assumed) 80% × 100 hours 80% × 80 hours 80% × 64 hours Unit Time to produce Calculation of time Impact: It causes the unit price to decrease as production increases. This implies a nonlinear model. LO 5-9 (Appendix B) Understand the mathematical relationship describing the learning phenomenon. LO 5-9

45. 5-45 End of Chapter 5