Chapter 3 activity cost behavior

4411
CHAPTER 3
ACTIVITY COST BEHAVIOR
QUESTIONS FOR WRITING AND DISCUSSION
1. Knowledge of cost behavior allows a man-
ager to assess changes in costs that result
from changes in activity. This allows a man-
ager to assess the effects of choices that
change activity. For example, if excess ca-
pacity exists, bids that at least cover variable
costs may be totally appropriate. Knowing
what costs are variable and what costs are
fixed can help a manager make better bids.
2. The longer the time period, the more likely
that a cost will be variable. The short run is a
period of time for which at least one cost is
fixed. In the long run, all costs are variable.
3. Resource spending is the cost of acquiring
the capacity to perform an activity, whereas
resource usage is the amount of activity ac-
tually used. It is possible to use less of the
activity than what is supplied. Only the cost
of the activity actually used should be as-
signed to products.
4. Flexible resources are those acquired from
outside sources and do not involve any long-
term commitment for any given amount of
resource. Thus, the cost of these resources
increases as the demand for them increas-
es, and they are variable costs (varying in
proportion to the associated activity driver).
5. Committed resources are acquired by the
use of either explicit or implicit contracts to
obtain a given quantity of resources, regard-
less of whether the quantity of resource
available is fully used or not. For multiperiod
commitments, the cost of these resources
essentially corresponds to committed fixed
costs. Other resources acquired in advance
are short term in nature and essentially cor-
respond to discretionary fixed costs.
6. Committed fixed costs are those incurred for
the acquisition of long-term activity capacity
and are not subject to change in the short
run. Annual resource expenditure is inde-
pendent of actual usage. For example, the
cost of a factory building is a committed
fixed cost. Discretionary fixed costs are
those incurred for the acquisition of short-
term activity capacity, the levels of which
can be altered quickly. In the short run, re-
source expenditure is also independent of
actual activity usage. An engineer’s salary is
an example of such an expenditure.
7. A variable cost increases in direct proportion
to changes in activity usage. A one-unit in-
crease in activity usage produces an in-
crease in cost. A step cost, however, in-
creases only as activity usage changes in
small blocks or chunks. An increase in cost
requires an increase in several units of activ-
ity. When a step cost changes over relatively
narrow ranges of activity, it may be more
convenient to treat it as a variable cost.
8. A step cost with narrow steps can be treated
as variable, while one with wide steps is typ-
ically treated as fixed.
9. An activity rate is the resource expenditure
for an activity divided by the activity’s prac-
tical capacity.
10. Mixed costs are usually reported in total in
the accounting records. How much of the
cost is fixed and how much is variable is un-
known and must be estimated.
11. A scattergraph allows a visual portrayal of
the relationship between cost and activity. It
reveals to the investigator whether a rela-
tionship may exist and, if so, whether a li-
near function can be used to approximate
the relationship. A scattergraph also can as-
sist in identifying any outliers.
12. Managers can use their knowledge of cost
relationships to estimate fixed and variable
components. A scattergraph can be used as
an aid in this process. From a scattergraph,
a manager can select two points that best
represent the relationship. These two points
can then be used to derive a linear cost for-
mula. The high-low method tells the manag-
er which two points to select to compute the
linear cost formula. The selection of these
two points is not left to judgment.
13. Because the scatterplot method is not re-
stricted to the high and low points, it is poss-
ible to select two points that better represent
the relationship between activity and costs,

4422
producing a better estimate of fixed and va-
riable costs. A scattergraph also identifies
outliers that could represent a high or low
point that is an aberration. The main advan-
tage of the high-low method is that it re-
moves subjectivity from the choice process.
The same line will be produced by two dif-
ferent people.
14. Assuming that the scattergraph reveals that
a linear cost function is suitable, then the
method of least squares selects a line that
best fits the data points. The method also
provides a measure of goodness of fit so
that the strength of the relationship between
cost and activity can be assessed.
15. The best-fitting line is the one that is “clos-
est” to the data points. This is usually meas-
ured by the line that has the smallest sum of
squared deviations.
16. No. The best-fitting line may not explain
much of the total cost variability. There must
be a strong relationship as well.
17. The coefficient of determination is the per-
centage of total variability in costs explained
by the activity. As such, it is a measure of
the goodness of fit, the strength of the rela-
tionship between cost and activity.
18. The correlation coefficient is the square root
of the coefficient of determination. The cor-
relation coefficient reveals the direction of
the relationship in addition to the strength of
the relationship.
19. If the variation in cost is not well explained
by activity usage (the coefficient of determi-
nation is low) as measured by a single driv-
er, then other explanatory variables may be
needed to build a good cost formula.
20. If the mixed costs are immaterial, then the
method of decomposition is unimportant.
Furthermore, sometimes managerial judg-
ment may be more useful for assigning
costs than the use of formal statistical me-
thodology.

4433
EXERCISES
3–1
1. Number of Units Total Cost Cost per Unit
0 $120,000 NA
50,000 120,000 $2.40
100,000 120,000 1.20
150,000 120,000 0 .80
200,000 120,000 0 .60
250,000 120,000 0 .48
2. Supervision cost is strictly fixed.
3–2
1. Miles Traveled Total Cost Cost per Mile
0 $ 0 $0.00
2,000 600 0.30*
4,000 1,200 0.30
6,000 1,800 0.30
8,000 2,400 0.30
10,0003,000 0.30
*$1,200/4,000 or $3,000/10,000 = $.30
2. The cost of fuel for the delivery activity is strictly variable.

4444
3–3
1.
Graph of Truck Depreciation
$0
$50,000
$100,000
$150,000
$200,000
$250,000
0 10 20 30 40 50 60 70 80 90 100
Cubic Yards of Concrete (in
thousands)
DepreciationCost
2.
Graph of Raw Materials Cost
0
1,000,000
2,000,000
3,000,000
1 2 3 4 5
Cubic yards of concrete
Costofraw
materials
Series2
3. Truck depreciation: Fixed cost
Raw materials cost: Variable cost
3-4
1. Number of Units Total Cost Cost per Unit
0 $10,000 NA
10,000 10,000 $1.00
20,000 10,000 0.50
30,000 20,000 0.67
40,000 20,000 0.50
50,000 30,000 0.60

4455
2. Forming machines rental cost is a step cost.
3-5
1.
Graph of Machining Direct Labor Cost
0
50000
100000
150000
200000
250000
300000
350000
0 1000 2000 3000 4000 5000
Number of units
CostofDirectLabor
The direct labor cost in the machining department is a step cost (with narrow
steps).
2.
Graph of Machining Department
Supervision Cost
0
50000
100000
150000
0 1000 2000 3000 4000 5000
Number of units
CostofSupervision
The cost of supervision for the machining department is a step cost (with wide
steps).

4466
3. Direct labor cost increase = $144,000 – $108,000 = $36,000
Supervision increase = $80,000 – $40,000 = $40,000
3-6
Cost Category Variable Cost Discretionary
Fixed Cost
Committed Fixed
Cost
Technician
salaries
X
Laboratory facility X
Laboratory
equipment
X
Chemicals and
other supplies
X
3–7
Resource Flexible/Committed Cost Behavior
Jet rental Committed Fixed
Hotel rooms Committed Fixed
Buffet Flexible Variable
Favor package Flexible Variable
Buses Committed Step
3–8
1. Resource Total Cost Unit Cost
Plastic1
$10,800 $0.027
Direct labor and
variable overhead2
8,000 0.020
Mold sets3
20,000 0.050
Other facility costs4
10,000 0.025
Total $48,800 $0.122
1
0.90 × $0.03 × 400,000 = $10,800; $10,800/400,000 = $0.027
2
$0.02 × 400,000 = $8,000; $8,000/400,000 = $0.02
3
$5,000 × 4 quarters = $20,000; $20,000/400,000 = $0.05
4
$10,000; $10,000/400,000 = $0.025
2. Plastic, direct labor, and variable overhead are flexible resources; molds and
other facility costs are committed resources. The cost of plastic, direct labor,
and variable overhead are strictly variable. The cost of the molds is fixed for

4477
the particular action figure being produced; it is a step cost for the production
of action figures in general. Other facility costs are strictly fixed.
3–9
1. Total maintenance cost = $24,000 + $0.30(200,000) = $84,000
2. Total fixed maintenance cost = $24,000
3. Total variable maintenance cost = $0.30(200,000) = $60,000
4. Total maintenance cost per unit = [$24,000 + $0.30(200,000)]/200,000
= $84,000/200,000
= $0.42
5. Fixed maintenance cost per unit = $24,000/200,000 = $0.12
6. Variable maintenance cost per unit = $0.30
7. Requirements1-6 repeated:
1. Total maintenance cost = $24,000 + $0.30(100,000) = $54,000
2. Total fixed maintenance cost = $24,000
3. Total variable maintenance cost = $0.30(100,000) = $30,000
4. Total maintenance cost per unit = [$24,000 + $0.30(100,000)]/100,000
= $54,000/100,000
= $0.54
5. Fixed maintenance cost per unit = $24,000/100,000 = $0.24
6. Variable maintenance cost per unit = $0.30

4488
3–10
1. Committed resources: trucks and technicians’ salaries
Flexible resources: supplies, small tools, and fuel
2. Variable activity rate = $420,000/35,000 = $12 per call
Fixed activity rate = $600,000*/40,000** = $15 per call
Total cost of one call = $12 + $15 = $27 per call
*($24,000 × 20) + ($10,000 × 12);
**8 × 250 × 20
3. Activity availability = Activity usage + Unused capacity
Calls available = Calls made + Unmade calls
40,000 calls = 35,000 calls + 5,000 calls
4. Total cost of Cost of Cost of
committed resources = activity used + unused capacity
$600,000 = ($15 × 35,000) + ($15 × 5,000)
$600,000 = $525,000 + $75,000
Note: The analysis is restricted to committed resources, since only these re-
sources will ever have any unused capacity.

4499
3–11
1. Committed resource charges: monthly fee, activation fee, cancellation fee (if
triggered by contract cancellation prior to one year)
Flexible resource charges: all additional charges for airtime, long distance
and roaming
2. Plan 1:
Minutes available = Minutes used + Unused minutes
60 minutes = 45 minutes + 15 minutes
Plan 2:
Plan 1 is more cost effective. Jana will have some unused capacity (on aver-
age, 15 minutes a month), and the overall cost will be lower by $10 per month.
3. Plan 1*:
60 minutes = 90 minutes + (− 30) minutes
Plan 1*:
Additional minutes = 30 minutes
*There are a number of ways to illustrate the use of minutes with Plan 1. Here
are two possibilities. The problem, of course, is that all included monthly
minutes are used, and Jana must purchase additional minutes.
Plan 2:
Plan 2 is now more cost effective, as the monthly cost is $30. Under Plan 1,
Jana will pay $20 plus $30 (30 minutes × $1.00) or $50 per month. (The $1.00
additional charge includes the airtime and regional roaming charge.)

5500
3-12
1.
Graph of Cost of Giving Opening Shows
0
1000
2000
3000
4000
5000
6000
7000
8000
0 5 10 15 20
Number of opening shows
Cost
This is a strictly variable cost.
2.
Graph of Cost of Running the Gallery
0
20000
40000
60000
80000
100000
0 5 10 15 20
Cost
This is a strictly fixed cost.

5511
3.
Graph of Ben's Total Costs
79000
80000
81000
82000
83000
84000
85000
86000
87000
88000
0 5 10 15 20
TotalCost
This is a mixed cost.
4. Total cost = $80,000 + $500(Number of opening shows)
5. Total cost = $80,000 + $500(12) = $86,000
Total cost = $80,000 + $500(14) = $87,000
3-13
1. The high point is March with 3,100 appointments. The low point is January with
700 appointments.
2. Variable rate = ($2,790 – $1,758)/(3,100 – 700)
= $1,032/2,400
= $0.43 per tanning appointment
Using the high point:
Fixed cost = $2,790 – $0.43(3,100) = $1,457
OR
Using the low point:
Fixed cost = $1,758 – $0.43(700) = $1,457
3. Total tanning service cost = $1,457 + $0.43 × Number of appointments
4. Total predicted cost for September = $1,457 + $0.43(2,500) = $2,532

5522
Total fixed cost for September = $1,457
Total predicted variable cost = $0.43(2,500) = $1,075
3-14
1.
Scattergraph of Tanning Services
0
500
1000
1500
2000
2500
3000
0 1000 2000 3000 4000
Number of appointments
MonthlyCost
Yes, it appears that there is a linear relationship between tanning cost and num-
ber of appointments.
2. Total cost of tanning services = $1,290 + $0.45 × Number of appointments
3. Total predicted cost for September = $1,290 + $0.45(2,500) = $2,415

5533
3–15
1.
Cost of Oil Changes
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
$8,000
$9,000
0 500 1,000 1,500
Number of Oil Changes
Cost
The scattergraph provides evidence for a linear relationship.
2. High (1,400, $7,950); Low (700, $5,150)
V = ($7,950 – $5,150)/(1,400 – 700)
= $2,800/700 = $4 per oil change
F = $5,150 – $4(700)
= $5,150 – $2,800 = $2,350
Cost = $2,350 + $4 (oil changes)
Predicted cost for January = $2,350 + $4(1,000) = $6,350

5544
3–15 Concluded
3. Output of the regression routine calculated by a spreadsheet:
Constant 1697.097
Std. Err. of Y Est. 243.6784
R Squared 0.967026
No. of Observations 8
Degrees of Freedom 6
X Coefficient(s) 4.64678
Std. Err. of Coef. 0.350304
Rounding the coefficients:
Variable rate = $4.65 per oil change
Fixed cost = $1,697
Predicted cost for January = $1,697 + $4.65 (oil changes)
= $1,697 + $4.65(1,000) = $6,347
R2
= 0.97 (rounded)
This says that 97 percent of the variability in the cost of providing oil changes
is explained by the number of oil changes performed.
4. The least-squares method is better because it uses all eight data points in-
stead of just two.

5555
3–16
1.
Cost of Moving Materials
$0
$2,000
$4,000
$6,000
$8,000
$10,000
$12,000
$14,000
$16,000
0 500 1,000
Number of Moves
The scattergraph provides evidence for a linear relationship, but the observa-
tion for 300 moves may be an outlier.
2. High (800, $14,560); Low (100, $3,000)
V = ($14,560 – $3,000)/(800 – 100)
= $11,560/700 = $16.51 per move (rounded)
F = $3,000 – $16.51(100)
= $3,000 – $1,651 = $1,349
Cost = $1,349 + $16.51 (moves)
Predicted cost = $1,349 + $16.51(550) = $10,430 (rounded)

5566
3–16 Concluded
3. Output of the regression routine calculated by a spreadsheet:
Constant 497.50
R Squared 0.926208
Rounding the coefficients:
Variable rate = $18.43 per move
Fixed cost = $498
Cost = $498 + $18.43 (moves)
= $498 + $18.43(550) = $10,635 (rounded)
R2
= 0.93 (rounded)
This says that 93 percent of the variability in the cost of moving materials is
explained by the number of moves.
4. Normally, we would prefer the least-squares method since the data appear to
be linear. However, the third observation may be an outlier. If the third obser-
vation (300 moves and $3,400 of cost) is dropped, the R
2
rises to 99 percent.
The new cost formula would be
Cost = $1,411 + $17.28 (moves)
The higher fixed cost is much more in keeping with what we observed with
the scatterplot in requirement 1.

5577
3–17
1. Maintenance cost = $5,750 + $16X
2. Maintenance cost = $5,750 + $16(650) = $5,750 + $10,400 = $16,150
3. To obtain the percentage explained, r needs to be squared: 0.89 × 0.89 = 79.21
percent. The relationship appears strong but perhaps could be improved by
searching for another explanatory variable. Leaving about 20 percent of the
variability unexplained may produce less than satisfactory predictions.
4. Maintenance cost = 12($5,750) + $16(8,400) = $69,000 + $134,400 = $203,400
Note: The fixed cost from the regression results is the fixed cost for the
month (since monthly data were used to estimate the equation). However, the
question asks for the cost for the year. Therefore, the fixed cost from the re-
gression equation must be multiplied by 12.
3–18
1. Overhead = $2,130 + $17(DLH) + $810(setups) + $26(purchase orders)
2. Overhead = $2,130 + $17(600) + $810(50) + $26(120)
= $2,130 + $10,200 + $40,500 + $3,120
= $55,950
3. Since total setup cost is $40,500 for the following month, a 50 percent de-
crease would reduce setup cost to $20,250, saving $20,250 for the month.

5588
3–19
1. Warranty repair cost = $2,000 + $60(number of defects) - $10(inspection
hours)
2. Warranty repair cost = $2,000 + $60(100) – $10(150) = $6,500
3. The number of defects is positively correlated with warranty repair costs. In-
spection hours are negatively correlated with warranty repair costs.
4. In this equation, the independent variables—number of defects and inspec-
tion hours—account for 88 percent of the variability in warranty repair costs.
It seems that analysts have identified some very good drivers for warranty re-
pair costs.

5599
PROBLEMS
3-20
a. Variable cost
b. Committed fixed cost
c. Discretionary fixed cost
d. Discretionary fixed cost
e. Discretionary fixed cost
f. Variable cost
g. Variable cost
h. Discretionary fixed cost
i. Discretionary fixed cost
j. Committed fixed cost
3-21
1.
Scattergraph of Receiving Activity
0
5000
10000
15000
20000
25000
30000
35000
0 500 1000 1500 2000
Number of receiving orders
ReceivingCost
Yes, the relationship appears to be reasonably linear.
2. Using the high-low method:
Variable receiving cost = ($27,000 – $15,000)/(1,700 – 700) = $12
Fixed receiving cost = $15,000 – $12(700) = $6,600
Predicted cost for 1,475 receiving orders:
Receiving cost = $6,600 + $12(1,475) = $24,300
3. Receiving cost for the quarter= 3($6,600) + $12(4,650)

6600
= $19,800 + $55,800
= $75,600
Receiving cost for the year = 12($6,600) + $12(18,000)
= $79,200 + $216,000
= $295,200
4. Receiving cost = $3,212 + $15.15 × Number of receiving orders
Receiving cost = $3,212 + $15.15(1,475) = $25,558
Receiving cost for the quarter = 3($3,212) + $15.15(4,650)
= $9,636 + $70,448
= $80,084
Receiving cost for the year = 12($3,212) + $15.15(18,000)
= $38,544 + $272,700
= $311,244
3-22
1. Results of regressions:
10 Months Data 12 Months Data
Intercept 3,212.121 3,820
Slope 15.15152 15.10
R2
0.8485 0.7451

6611
2.
Scattergraph of Receiving Activity -
12 Months Data
0
5000
10000
15000
20000
25000
30000
35000
0 500 1000 1500 2000
Number of receiving orders
Receivingcost
The point for the 11th month (1,200 receiving orders and $28,000 total receiving
cost) appears to be an outlier. Since the cost was so much higher in this month
due to an event that is not expected to happen again, this data point could easily
be dropped. Then, data from the 11 remaining months could be used to develop a
cost formula for receiving cost.

6622
3. Results for the method of least squares after dropping month 11.
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.926737
R Square 0.858841
Adjusted R
Square 0.843157
Standard Error 2051.781
Observations 11
ANOVA
df SS MS F
Significance
F
Regression 1 2.31E+08 2.31E+08 54.7581 4.1E-05
Residual 9 37888233 4209804
Total 10 2.68E+08
Coeffi-
cients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 3168.56 2565.262 1.23518 0.248035 -2634.47 8971.589 -2634.47 8971.589
X Variable 1 15.17946 2.051314 7.399872 4.1E-05 10.53906 19.81986 10.53906 19.81986
Receiving cost = $3,168.56 + $15.18 × Number of receiving orders
Predicted receiving cost for a month
= $3,168.56 + $15.18(1,475) = $25,559.06
The regression run on the 11 months of data from “typical” months appears to be
better than the one for all 12 months. R2
is higher for the regression without the
outlier (85.88 percent versus 74.512 percent), and the scattergraph gives Joseph
confidence that the data without the outlier describe a relatively linear relation-
ship. Since the storm damage is not expected to recur, month 11 can safely be
dropped from a regression meant to help predict future receiving cost.

6633
3–23
1. Salaries:
Senior accountant—fixed
Office assistant—fixed
Internet and software subscriptions—mixed
Consulting by senior partner—variable
Depreciation (equipment)—fixed
Supplies—mixed
Administration—fixed
Rent (offices)—fixed
Utilities—mixed
2. Internet and software subscriptions:
V = (Y2 – Y1)/(X2 – X1)
= ($850 – $700)/(150 – 120) = $5 per hour
F = Y2 – VX2
= $850 – ($5)(150) = $100
Consulting by senior partner:
V = (Y2 – Y1)/(X2 – X1)
= ($1,500 – $1,200)/(150 – 120) = $10 per hour
F = Y2 – VX2
= $1,500 – ($10)(150) = $0
Supplies:
V = (Y2 – Y1)/(X2 – X1)
= ($1,100 – $905)/(150 – 120) = $6.50 per hour
F = Y2 – VX2
= $1,100 – ($6.50)(150) = $125
Utilities:
V = (Y2 – Y1)/(X2 – X1)
= ($365 – $332)/(150 – 120) = $1.10 per hour
F = Y2 – VX2
= $365 – ($1.10)(150) = $200

6644
3–23 Concluded
3. Unit
Fixed Variable Cost
Salaries:
Senior accountant $2,500 $ —
Office assistant 1,200 —
Internet and subscriptions 100 5.00
Consulting — 10.00
Depreciation (equipment) 2,400 —
Supplies 125 6.50
Administration 500 —
Rent (offices) 2,000 —
Utilities 200 1.10
Total cost $9,025 $22.60
Thus, total clinic cost = $9,025 + $22.60/professional hour
For 140 professional hours:
Clinic cost = $9,025 + $22.60(140) = $12,189
Charge per hour = $12,189/140 = $87.06
Fixed charge per hour = $9,025/140 = $64.46
Variable charge per hour = $22.60
4. For 170 professional hours:
Charge/day = $9,025/170 + $22.60 = $53.09 + $22.60 = $75.69
The charge drops because the fixed costs are spread over more professional
hours.

6655
3–24
1. High (1,700, $21,000); Low (700, $15,000)
V = (Y2 – Y1)/(X2 – X1)
= ($21,000 – $15,000)/(1,700 – 700) = $6 per setup
F = Y2 – VX2
= $21,000 – ($6)(1,700) = $10,800
Y = $10,800 + $6X
2. Output of spreadsheet regression routine with number of setups as the inde-
pendent variable:
Constant 4512.98701298698
Std. Err. of Y Est. 3456.24317476605
R Squared 0.633710482694768
X Coefficient(s) 13.3766233766234
Std. Err. of Coef. 3.59557461331427
V = $13.38 per receiving order (rounded)
F = $4,513 (rounded)
Y = $4,513 + $13.38X
R2
= 0.634, or 63.4%
Setups explain about 63.4 percent of the variability in order filling cost, pro-
viding evidence that Brett’s choice of a cost driver is reasonable. However,
other drivers may need to be considered because 63.4 percent may not be
strong enough to justify the use of only receiving orders.

6666
3–24 Continued
3. Regression with setup hours as the independent variable:
Constant 5632.28109733183
Std. Err. of Y Est. 2390.10628259277
R Squared 0.824833789433823
X Coefficient(s) 4.49642991356633
V = $4.50 per setup hour
F = $5,632 (rounded)
Y = $5,632 + $4.50X
R2
= 0.825, or 82.5%
Setup hours explain about 82.5 percent of the variability in order filling cost.
This is a better result than that of setups and should convince Brett to try
multiple regression.

6677
3–24 Concluded
4. Regression routine with pounds of material and number of receiving orders
as the independent variables:
Constant 752.104072925631
Std. Err. of Y Est. 1350.46286973443
R Squared 0.951068418023306
X Coefficient(s) 3.33883151096915 7.14702865269395
Std. Err. of Coef. 0.495524841198368 1.68182916088492
V1 = $3.34 per pound of material delivered (rounded)
V2 = $7.147 per receiving order (rounded)
F = $752 (rounded)
Y = $752 + $3.34a + $7.147b
R2
= 0.95, or 95%
Multiple regression with both variables explains 95 percent of the variability
in receiving cost. This is the best result.
3–25
1. The order should cover the variable costs described in the cost formulas.
These variable costs represent flexible resources.
Materials ($94 × 20,000) $1,880,000
Labor ($16 × 20,000) 320,000
Variable overhead ($80 × 20,000) 1,600,000
Variable selling ($7 × 20,000) 140,000
Total additional resource spending $3,940,000
Divided by units produced ÷ 20,000
Total unit variable cost $ 197
Garner should accept the order because it would cover total variable costs
and increase income by $15 per unit ($212 – $197), for a total increase of
$300,000.

6688
3–25 Concluded
2. The correlation coefficients indicate the reliability of the cost formulas. Of the
four formulas, overhead activity may be a problem. A correlation coefficient
of 0.75 means that only about 75 percent of the variability on overhead cost is
explained by direct labor hours. This should have a bearing on the answer to
Requirement 1 because if the percentage is low, there are activity drivers oth-
er than direct labor hours that are affecting variability in overhead cost. What
these drivers are and how resource spending would change need to be
known before a sound decision can be made.
3. Resource spending attributable to order:
Material ($94 × 20,000) $ 1,880,000
Labor ($16 × 20,000) 320,000
Variable overhead:
($85 × 20,000) 1,700,000
($5,000 × 12) 60,000
($300 × 600) 180,000
Variable selling ($7 × 20,000) 140,000
Total additional resource spending $ 4,280,000
Divided by units produced ÷ 20,000
Total unit variable cost $ 214
The order would not be accepted now because it does not cover the variable
activity costs. Each unit would lose $2 ($212 – $214).
It would also be useful to know the step-cost functions for any activities that
have resources acquired in advance of usage on a short-term basis. It is
possible that there may not be enough unused activity capacity to handle the
special order, and resource spending may also be affected by a need (which,
in this case, would be unexpected) to expand activity capacity.

6699
3–26
1. High (2,000; $120,000); Low (1,200; $52,000)
V = ($120,000 – $52,000)/(2,000 – 1,200) = $85/nursing hour
F = $52,000 – ($85 × 1,200) = –$50,000
This problem illustrates how the high-low method can be misleading when
cost behavior patterns have changed. Fortunately, in this case, the negative
value of fixed cost tells us that something is wrong.
2. a. Output of spreadsheet multiple regression routine:
Constant 236.211171346831
Std. Err. of Y Est. 1788.59942408259
R Squared 0.993939842186014
X Coefficient(s) 40.8752113255057 35307.5122042085
Std. Err. of Coef. 2.2207348945557 970.201096681915
b. Output of spreadsheet regression routine on 2008 data:
Constant 10081.3333333337
Std. Err. of Y Est. 94.8068211329403
R Squared 0.999887905585866
X Coefficient(s) 34.9533333333331
Std. Err. of Coef. 0.151087766637518

7700
3–26 Concluded
c. Output of spreadsheet regression routine on 2009 data:
Constant 19964.2403242688
Std. Err. of Y Est. 12.0521931978647
R Squared 0.999999089146329
X Coefficient(s) 50.0216788702923
Std. Err. of Coef. 0.0238700194326353
While each regression has a high R2
, the multiple regression gives unaccept-
able results. Notice the $35,308 coefficient on the independent variable
“changes.” Yet, the increased fixed cost was only $10,000 per month. Re-
gression (c) gives more reasonable results. The intercept term, $19,964, is
roughly $10,000 higher than the intercept term for Regression (b), as ex-
pected. So, the hospital should use Regression (c) to budget for the rest of
the year.
3–27
1. Output of spreadsheet regression with pounds as independent variable:
Constant 4,997.2877
R Squared 0.9315
Budgeted setup cost at 5,200 pounds:
Y = $4,997.29 + $2.51(5,200)
= $18,033.24

7711
3–27 Continued
2. Output of spreadsheet regression with number of orders as the independent
variable:
Constant 17,485.8088
R Squared 0.01327
Budgeted setup cost for 160 orders:
Y = $17,485.81 + $6.05(160)
= $18,453.81
3. The regression equation based on pounds is better because the coefficient of
determination is much higher. Pounds explain about 93 percent of the varia-
tion in receiving costs, while number of orders explains only 1.3 percent of
the variation in receiving costs.

7722
3–27 Concluded
4. Output of spreadsheet for multiple regression:
Constant 2986.529
R Squared 0.9982
X Coefficient(s) 2.6056 13.7142
Std. Err. of Coef. 0.0453 0.9163
Y = $2,986.53 + $2.61(5,200) + $13.71(160)
= $18,729.81
The explanatory power of both variables is very high. Yet, pounds seems to
explain most of the variation, and the use of one driver would vastly simplify
budgeting and product costing. The increased complexity is probably not
worth adding the second driver.

7733
MANAGERIAL DECISION CASE
3–28
1. Jackie violated the standard of confidentiality. Management accountants
should not disclose confidential information acquired in the course of their
work unless legally obligated to do so. Her motives for disclosing the confi-
dential information apparently were intended to further her personal interests.
Management accountants are prohibited from using confidential information
for unethical advantages. In addition, some could argue that Jackie also vi-
olated the standard of integrity. Conflict of interest, receipt of favors or gifts,
and subversion of an organization’s pursuit of its legitimate objectives all
could be in violation.
2. Assuming that the data were acquired illicitly, Brindon’s instincts were on
target. To analyze the data and be party to its use would most certainly violate
the standard of integrity. Management accountants should not engage in or
support any activity that would discredit the profession. In addition, Brindon
would violate the standard of confidentiality if he chose to analyze the data.
Management accountants should refrain from using confidential information
acquired in the course of their work for unethical advantage, either personally
or through a third party (II-3).
RESEARCH ASSIGNMENT
3–29
Answers will vary.

Chapter 3 activity cost behavior

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