Agenda Today – Session 7
 Go over E2-28 and comment on
improvements
 Comment on practice quiz question six
 Learning curve problems
A&MIS 525
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E2-28
Canseco Company
Statement of Earnings (000's)
For the year ended December 31, 2004
Revenues
Cost of Goods sold
Gross margin
Operating expenses:
Marketing, distribution and customer service
General and administrative
Income from operations
$ 300,000
131,000
$ 169,000
$ 93,000
29,000
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122,000
$ 47,000
100.0%
43.7%
56.3%
31.0%
9.7%
40.7%
15.7%
2
Canseco Company
Schedule of Cost of Goods Manufactured and Sold (000's)
For the year ended December 31, 2004
Direct materials
Direct labor
Indirect manufacturing costs (Exhibit A)
Current cost of production
Work-in-process, Jan. 1, 2004
Total manufacturing cost to account for
Work-in-process, Dec. 31, 2004
Cost of goods manufactured
Finished goods, Jan. 1, 2004
Total goods available for sale
Finished goods, Dec. 31, 2004
Cost of goods sold
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$ 71,000
25,000
39,000
$ 135,000
21,000
$ 156,000
20,000
$ 136,000
18,000
$ 154,000
23,000
$ 131,000
45.5%
16.0%
25.0%
86.5%
13.5%
100.0%
12.8%
87.2%
11.5%
98.7%
14.7%
84.0%
3
Exhibit A
Canseco Company
Schedule of Indirect Manufacturing Costs
For the year ended December 31, 2004
Indirect manufacturing labor
Plant insurance
Depreciation
Repairs and maintenance
Total indirect manufacturing costs
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$
$
15,000
9,000
11,000
4,000
39,000
4
CAS Problem
Computation of the estimated
average manufacturing cost
of
the
first
500
units
produced:
y = ax-b
= $400(500-0.234465)
= $400(0.2329085)
=
$93.16
(rounded)
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CAS Problem
2. Computation of the estimated
total manufacturing cost of
the first 500 unit produced:
• xy = xax-b
= ax1-b
• = $400(5001-0.234465)
• = $46,582 (rounded)
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CAS Problem
3. Computation of the estimated
total manufacturing cost of
the first 750 unit produced:
y = $400(7501-0.234465)
= $400(158.84)
= $63,536 (rounded)
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CAS Problem
4. Computation of the estimated
incremental cost of producing
250 units after having
produced a total of 500 units:
x2y2 - x1y1 =
$400(75010.234465) $400(5001-0.234465)
= $400(158.84) $400(116.455)
= $63,536
- $46,582 8
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CAS Problem
5. Doubling the cost of the
first unit produced would
have the effect of doubling
all cost computations using
the learning curve model.
For requirement 2, the total
cost of 500 units would
become $93,164, or twice the
$46,582 computed using a cost
of $400 for the first unit
produced. Thus, having an
A&MIS 525
accurate estimate
of the cost 9
From One Model to Another
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Regression Analysis
 Used to estimate the parameters
of a linear equation in one or more
variables. In the context of cost
estimation, we are need to
estimate fixed and variable costs.
 Regression analysis uses all available
data to estimate the cost function.
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Regression Analysis
 Simple regression analysis
estimates the relationship between
the dependent variable and one
independent variable.
 Multiple regression analysis
estimates the relationship between
the dependent variable and
multiple independent variables.
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Regression Analysis
 The regression equation is
derived using the least-squares
technique (OLS).
 The objective of least-squares is
to develop estimates of the
parameters a and b for the line
y = a + bx or TC = F + vQ
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Regression Analysis
 The vertical difference (residual
term) measures the distance
between the actual cost and the
estimated cost for each volume
observation.
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Learning Objective 5
Describe the criteria
to evaluate and choose
cost drivers
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Criteria to Evaluate and Choose
1.
2.
3.
4.
Economic plausibility
Consistent with design of the system
Goodness of fit of model of cost
Slope of the regression line (economic
and statistical significance)
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Goodness of Fit
 The coefficient of determination
(r2) expresses the extent to which
the changes in (x) explain the
variation in (y).
 An (r2) of 0.80 indicates that more
than 80 percent of the change in
the dependent variable can be
explained by the change in the
independent variable.
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Slope of Regression Line
 A relatively steep slope indicates a
strong relationship between the
cost driver and costs.
 A relatively flat regression line
indicates a weak relationship
between the cost driver and costs.
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Slope of Regression Line
 The closer the value of the
correlation coefficient (r) to ±1, the
stronger the statistical relation
between the variables and the
greater is R2.
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Slope of Regression Line
 As (r) approaches +1, a positive
relationship is implied, meaning
the dependent variable (y)
increases as the independent
variable (x) increases.
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Slope of Regression Line
 As (r) approaches –1, a negative,
or inverse, relationship is implied,
meaning the dependent variable
(y) decreases as the independent
variable (x) increases.
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Why Use Regression Analysis?
 The regression method is more
accurate than the high-low
method.
 All the available data is used to
make estimates, so the results are
less sensitive to the extreme
observations as in the high-low
method.
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Why Use Regression Analysis?
 Provides a measure of fit, r2
 Provides measures of estimation
error
 Provides probability estimates of
the statistical significance of the
parameters.
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