DSS-ESTIMATING COSTS
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11-2
Introduction
Cost
behavior
Cost
estimation
Cost
prediction
Existing
relationship
between
cost and
activity.
Process of
estimating
relationship
between costs
and cost driver
activities that
cause
those costs.
Using results of
cost estimation
to forecast a
level of cost at
a particular
activity. Focus
is on the future.
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Reasons for Estimating Costs
What will my
costs be if I introduce
the new model in a
foreign market?
How much
will costs increase
if sales increase
10 percent?
Management needs
to know the costs that
are likely to be
incurred for each
alternative.
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Reasons for Estimating Costs
Accurate
Cost
Estimates
Better
Decisions
Add Value
Improved
Decision
Making
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Exh.
11-1
11-5
Reasons for Estimating Costs
1. First,
identify this
Relationship
between
activities
and costs
3. To reduce
these
Costs
We estimate
costs to:
2. Then
manage these
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Activities
manage costs
make decisions
plan & set
standards
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Exh.
11-2
11-6
One Cost Driver and
Fixed/Variable Cost Behavior
TC = $190 + (.16 x Miles Driven)
600
510
500
Cost
400
350
300
200
190
$.16
Slope = Cost
Driver Rate
Intercept = Fixed
Cost
100
0
0
1000
2000
3000
Miles driven per month
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Nonlinear Costs
Total Cost
Curvilinear
Cost Function
Relevant Range
A straight-Line
(constant unit variable
cost) often closely
approximates a
nonlinear line within
the relevant range.
Activity
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The High-Low Method
The high-low method uses two points to
estimate the general cost equation TC = F  VX
TC = the value of the estimated
total cost
F = a fixed quantity that represents
the value of Y when X = zero
V = the slope of the line, the unit
variable cost .
X = units of the cost driver activity.
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11-9
The High-Low Method
Total Cost in
1,000s of Dollars
The high-low method uses two
points to estimate the general
cost equation TC = F + VX
20
10
* *
* *
* ** *
**
The two points should be representative of
the cost and activity relationship over the range
of activity for which the estimation is made.
0
0
1
2
3
4
Activity, 1,000s of Units Produced
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11-10
The High-Low Method
WiseCo recorded the following production activity
and maintenance costs for two months:
High activity level
Low activity level
Change
Units
9,000
5,000
4,000
Cost
$ 9,700
6,100
$ 3,600
Using these two levels of activity, compute:
 the variable cost per unit;
 the fixed cost; and then
 express the costs in equation form TC = F + VX.
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11-11
The High-Low Method
High activity level
Low activity level
Change
Units
9,000
5,000
4,000
Cost
$ 9,700
6,100
$ 3,600
 Unit variable cost = $3,600 ÷ 4,000 units = $.90 per unit
 Fixed cost = Total cost – Total variable cost
Fixed cost = $9,700 – ($.90 per unit × 9,000 units)
Fixed cost = $9,700 – $8,100 = $1,600
 Total cost = Fixed cost + Variable cost (TC = F + VX)
TC = $1,600 + $0.90X
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11-12
Regression Analysis
A statistical method used to create an
equation relating dependent (or Y) variables
to independent (or X) variables.
Past data is used to estimate relationships
between costs and activities.
Independent variables
are the cost drivers that
drive the variation in
dependent variables.
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Before doing the
analysis, take time to
determine if a logical
relationship between the
variables exists.
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11-13
Regression Analysis
The objective of the regression method is still a
linear equation to estimate costs TC = F + VX
TC = value of the dependent variable, estimated cost
F = a fixed quantity, the intercept, that
represents the value of TC when X = 0
V = the unit variable cost, the coefficient of
the independent variable measuring the
increase in TC for each unit increase in X
X = value of the independent variable, the cost driver
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Regression Analysis
A statistical procedure that finds the unique line
400
through data points that minimizes the sum of
squared distances from the data points to the line.
350
300
250
200
50
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100
150
Independent Variable
200
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11-15
Regression Analysis
V = the slope of the regression line or the
coefficient of the independent variable, the
increase in TC for each unit increase in X.
400
350
300
250
200
F = a fixed quantity, the intercept
50
McGraw-Hill/Irwin
100
150
Independent Variable
200
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11-16
Regression Analysis
The correlation coefficient, r, is a measure of the linear
relationship between variables such as cost and activity.
Total Cost
20
* *
* *
10
The correlation coefficient is highly
positive (close to 1.0) if the data points
are close to the regression line.
0
0
McGraw-Hill/Irwin
* ** *
**
1
2
3
Activity
4
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11-17
Regression Analysis
The correlation coefficient, r, is a measure of the linear
relationship between variables such as cost and activity.
*
Total Cost
20
*
*
*
*
*
*
*
The correlation coefficient is near
*
10
*
zero if little or no relationship
exists between the variables.
0
0
McGraw-Hill/Irwin
1
2
3
Activity
4
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11-18
Regression Analysis
The correlation coefficient, r, is a measure of the linear
relationship between variables such as cost and activity.
*
Total Cost
20
10
* *
*
*
*
*
* *
*
This relationship has a negative
correlation coefficient, approaching
a maximum value of –1.0
0
0
McGraw-Hill/Irwin
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2
3
Activity
4
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11-19
Regression Analysis
400
R2, the coefficient of determination, is a measure
of the goodness of fit. R2 tells us the amount
of the variation of the dependent variable that
is explained by the independent variable.
350
300
250
Regression with
high R2 (close to 1.0)
200
50
McGraw-Hill/Irwin
100
150
Independent Variable
200
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Regression Analysis
400
The coefficient of
determination, R2,
is the correlation
coefficient squared.
350
300
250
Regression with
low R2 (close to 0)
200
50
McGraw-Hill/Irwin
100
150
Independent Variable
200
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11-21
Regression Analysis
Uses all data points
resulting in a better
relationship between the
variables.
Generates statistical
information that describes
the relationship between
variables.
Permits the use of more
than one cost driver activity
to explain cost behavior.
McGraw-Hill/Irwin
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