CHAPTER 2
Measurement of Cost behavior
Cost behavior
The relationship between costs and activity levels.
Cost behavior patters/cost functions:
Y=a+bX where:
Y= Total Costs
a= Total FC
b=VC/unit of the activity variable
X= Volume of the activity variable
Criteria for choosing a cost function/activity variable
Use activity analysis to determine which cost driver
best explains how the cost behaves:
Economic plausibility (it must make sense that X
causes Y)
Reliability (the estimates derived by the cost
equation must conform with actually observed
costs)
1. (VC) Total VC
y = a+bx
b = 1.20/unit
y = 0 + 1.20x
a=0
$
units
2. (VC) Step VC - increases in small steps
(not in textbook)
# of golfers
1-10
11-20
21-30
…
Maintenance
hours ($)
$20
$40
$60
…
$
100
80
60
40
20
10
20
30
40
50
golfers
3. (FC) Total FC
a = 10,000 b = 0
y = 10,000 + 0x
$
10,000
units
4. (FC) Step FC-different ranges
$
100,000
75,000
50,000
50,000
100,000
relevant
range
150,000
units
5. Semi-variable or Mixed costs
- contain both VC and FC
y = 15,000 + 1x
$
Total cost
VC
15,000
FC
Units
6. Curvilinear cost line
- marginal cost will first decrease,
then increase as activity level increases
`
a
relevant
range
Types of fixed costs
Discretionary costs: Fixed costs arising from annual
management decisions. Ex. Advertising costs
Committed costs: Fixed costs which are long term
investments. Ex. Buildings, equipment costs
Cost estimation
The process of determining how a particular cost
behaves.
3 techniques to estimate semi variable or mixed costs.
Scatter graph / visual fit method
11,700
1. Scattergraph / Visual fit
method
y = a + bx
11,700 = 9,700 + b(2,000)
b = $1/unit
Y = 9,700 + 1x
9,700
Scatter graph / visual fit method
❖ Pros:
➢ Simplicity
➢ Brings attention to outliers (points that fall far away from
the rest of the data)
➢ Uses all data points
➢ Shows correlation between the dependent and
independent variables
➢ Shows cost behavior at different ranges of activity
Cons
➢ Subjective
High low method
Uses 2 data points-the highest and lowest ACTIVITY
LEVELS and their CORRESPONDING COSTS.
VC/unit or b=change in total costs/change in activity
levels
FC or a=Y-b(X) where Y and X are taken from either the
highest or lowest activity level.
High low method
❖ Pros:
➢ Objective
➢ Simplicity
❖ Cons:
➢ Only use 2 data points
High low method
2. High low method
VC/unit or b
𝟏𝟐, 𝟏𝟐𝟎 − 𝟏𝟎, 𝟐𝟎𝟎
= $𝟏. 𝟐𝟎/𝒖𝒏𝒊𝒕
𝟐, 𝟔𝟎𝟎 − 𝟏, 𝟎𝟎𝟎
a = 12,120 = a + 1.20 (2,600) = 9,000
y = 9,000 + 1.20x
Least squared regression
Use statistics to determine the best fit line so that the
sum of the squared deviations is minimized.
Use the goodness of fit measurement measured by the
Coefficient of Determination or R squared to determine
the correlation of the dependent and independent
variables.
Least squared regression
❖ Pros:
➢ Objective
➢ Uses all data points
➢ Uses statistics to determine the best fit line
❖ Cons:
➢ Extensive calculations required
➢
Can be severely influenced by outliers
➢ Must also check for economic plausibility
Least squared regression
3. Least squares regression
y = 9,943 + 0.885x
R2 = range 0-1
Close to 0
Close to 1
.
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. . .
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Assumptions
Costs must be linear within the relevant range
Only one activity variable can be used at a time.
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