2. COST BEHAVIOR USING A VARIETY OF METHODS Rose Nchang

3. Data – Braunhaus Micro Brewery MonthProcessing Hours CasesMOH CostMOH Cost/ Processing Hour MOH Cost Per Case January68080002950043.383.69 February57567502780048.354.12 March50055002450049.004.45 April60072502900048.334.00 May65078002800043.083.59 June71056002975041.905.31

4. Mixed Costs Are manufacturing Overhead Costs fixed, variable or mixed?  The manufacturing overhead costs are mixed.

5. Graph against hours Overhead Costs against Processing Hours

6. Graph against Cases Overhead Costs against Cases Produced

7. Sound Data? Between the two sets of data, one appears to be more sound than the other. Using the processing hours as our driver (top graph) appears to offer better data than using the cases produced.

8. Question 5 High Cost: 29750 Low Cost: 24500 High Processing Hours: 710 Low Processing Hours: 500  Slope: 29750-24500 710-500 Slope=$25  Vertical Intercept: Y = VX + F 29750= 25(710) +F F = 12000 24500= 25(500) + F F = 12000  Equation: Y = 25x + 12000 DATACalculations

9. Question 6 Manufacturing Overhead in July will be; If Y = 25x + 12000, and Microbrewery incurred 550 processing hours in July, Therefore MOH July will Be Y = 25(550) + 12000 Y = 25,750

10. Question 7 The R-Square value of 79.07% is somewhat sound. Generally, an R square greater than.80 means the cost equation is very reliable in predicting cost at a relevant range. But an R square below.80 means managers need to use the cost equation with caution.

11. Question 7 concluded Slope: 22 Intercept: 14266 Hours Incurred: 550 Y= 22x + 14266 Y= 22(550) + 14266  Y= 26366

12. Question 8 The R-Square value in this analysis, using the cases produced as the driver, does not give reliable cost analysis data. Managers should consider other options if they come across data that does not fit.

13. Question 8 concluded  Slope: 0.81  Intercept: 22,541  Cases produced: 6000  Y = 0.81x + 22541  Y = 0.81(6000) + 22541  Y= 27,401 Which analysis is better? Question 7. Using the hours as a cost driver is a better fit than cases produced. The regression analysis shows using the hours as a better fit by over 50%.

14. Question 9 When we removed June as a potential outlier, our graph updated and presented a much better fit for the data. This is the regression analysis for that data.

15. Question 9  Slope: 1.78  Intercept: 15180.24  Cases Produced: 6000  Y=1.78x+15180.24  Y=1.78(6000)+15180.24  Y=25860.24

16. Which is Best? When we figured out the cases produced per hour for every month, June turned out to be somewhat of an anomaly. Every other month was between 11 and 12 cases an hour, June only 7. So we decided to erase June and run an analysis on the two drivers again. Here are the numbers as they compare to our original analysis - by hours.

17. The winner is…..