Required:

$(1).$ Evaluate the possible cost functions using the following criteria, $($a$)$ economic

plausibility, $($b$)$ Goodness$-$of$-$fit and $($c$)$ individual tests $($t tests$)$ for the cost parameters

at $95\%$ level of accuracy. Use the following format for the analysis.

Criterion Model $(1)....$ Model $($N$)$

a$)$ Economic

plausibility

b$)$ Goodness$-$of$-$fit

c$)$ Individual t tests

for cost parameters

$(2).$ Which model would you recommend and why?

$(3).$ For the model with two cost drivers, predict the indirect manufacturing labour cost

with $98$ hours of machining and $60$ hours of assembly labour.

$(4).$ Prepare a $99\%$ confidence interval for the forecast indirect manufacturing labour

cost in $(3)$ above. Explain

Transcribed Image Text:

Week Indirect manufacturing labour costs (1) 1 1190 XYZ logistics is developing a cost formula for allocating indirect manufacturing labor cost. Management has identified that indirect manufacturing labor cost may be driven by machine hours and/or direct labor hours. You have been given the following data. Alternative cost driver: direct manufacturing labour-hours (3) 30 Cost Driver: machine-hours (2) 68 2 1211 88 35 3 1004 62 36 4 917 72 20 5 770 60 47 6 1456 96 45 7 1180 78 44 8 710 46 38 9 1316 82 70 10 1320 94 30 11 752 68 29 12 963 48 38 Assume your role as a management accountant who has recently learnt regression analysis and is interested in further understanding the role of predictive analytics in cost management. Week Indirect manufacturing labour costs (1) 1 1190 XYZ logistics is developing a cost formula for allocating indirect manufacturing labor cost. Management has identified that indirect manufacturing labor cost may be driven by machine hours and/or direct labor hours. You have been given the following data. Alternative cost driver: direct manufacturing labour-hours (3) 30 Cost Driver: machine-hours (2) 68 2 1211 88 35 3 1004 62 36 4 917 72 20 5 770 60 47 6 1456 96 45 7 1180 78 44 8 710 46 38 9 1316 82 70 10 1320 94 30 11 752 68 29 12 963 48 38 Assume your role as a management accountant who has recently learnt regression analysis and is interested in further understanding the role of predictive analytics in cost management.