Cashunda Buckner_ACG3351-12-Week-1.docx

Cashunda Buckner_ACG3351-12-Week-1.docx - Cashunda Buckner...

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Cashunda Buckner Week 1 Assignment Chapter 10 and 11 10-20 1. The electricity cost is variable because, in each month, the cost divided by the number of kilowatt hours equals a constant $0.30. The definition of a variable cost is one that remains constant per unit. The telephone cost is a mixed cost because the cost neither remains constant in total nor remains constant per unit. The water cost is fixed because, although water usage varies from month to month, the cost remains constant at $120. 2. The month with the highest number of telephone minutes is June, with 2,880 minutes and $197.60 of cost. The month with the lowest is April, with 1,960 minutes and $179.20. The difference in cost ($197.60 – $179.20), divided by the difference in minutes (2,880 – 1,960) equals $0.02 per minute of variable telephone cost. Inserted into the cost formula for June: $197.60 = a fixed cost + ($0.02 × number of minutes used) $197.60 = a + ($0.02 × 2,880) $197.60 = a + $57.60 a = $140 monthly fixed telephone cost Therefore, Luwak’s cost formula for monthly telephone cost is Y = $140 + ($0.02 × number of minutes used) 3. The electricity rate is $0.30 per kw hour The telephone cost is $140 + ($0.02 per minute) The fixed water cost is $120. Adding them together we get: Fixed cost of utilities = $140 (telephone) + $120 (water) = $260 Monthly Utilities Cost = $130 + (0.30 per kw hour) + ($0.02 per telephone min.) 4. Estimated utilities cost = $260 + ($0.30 × 4,400 kw hours) + ($0.02 × 3,000 minutes) = $260 + $1,320 + $60 = $1,640
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10-22 10-33 1. Both number of units inspected, and inspection labor-hours are plausible cost drivers for inspection costs. The number of units inspected is likely related to test-kit usage, which is a significant component of inspection costs. Inspection labor-hours are a plausible cost driver if labor hours vary per unit inspected because costs would be a function of how much time the inspectors spend on each unit. This is particularly true if the inspectors are
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paid a wage, and if they use electric or electronic machinery to test the units of product (cost of operating equipment increases with time spent). Goodness of fit. As you can see from the two graphs, the regression line based on number of units inspected better fits the data (has smaller vertical distances from the points to the line) than the regression line based on inspection labor-hours. The activity of inspection appears to be more closely linearly related to the number of units inspected than inspection labor-hours. Hence number of units inspected is a better cost driver. This is probably because the number of units
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