Chapter 10
Cost Estimation
Prepared by
Diane Tanner
University of North Florida
Cost Estimation
 Purpose is to measure a relationship based on
data from past costs and the related level of an
 i. e., determine the cost equation
 A cause-and-effect relationship may arise as a
result of
 A physical relationship between the level of activity
and costs
 A contractual arrangement
 Knowledge of operations
Cost Functions
 Format of cost function
 TC = VCx + TFC
 Y = mX + b
 Y = a + bX
M = VC per unit = slope
b = TFC = y-intercept
 Dependent variable = y
 Independent variable = x
 Assumptions
 Variations in the level of a single activity (cost driver)
explain the variation in the related total cost
 Costs behave in a linear manner within the relevant
range of activity
 A linear cost function is represented by a straight line
Sample Cost Functions
 Fixed cost function
 Y = 250
 Variable cost function
 Y = 4.56X
 Mixed cost function (semi-variable)
 Y = 4.56X + 250
Cost Estimation Methods
 Industrial engineering method
 Estimates cost functions by analyzing the relationship
between inputs and outputs in physical terms
 Conference method
 Estimates cost functions based on analysis and opinions
about costs and their drivers gathered from various
departments of a company
 Account analysis method
 Estimates cost functions by classifying various cost
accounts with respect to the identified level of activity
 Reasonably accurate, cost-effective, and easy to use
 Quantitative analysis methods
 Mathematical models – high-low, regression
Quantitative Analysis of
Cost Functions
Step 1: Identify the dependent variable
 The cost being estimated or predicted, Y
Step 2: Identify the independent variable
 The independent variable, i.e., the cost driver, X
Step 3: Collect data
Step 4: Plot the data - scattergraph
Step 5: Estimate the function
Regression method or high-low method
Step 6: Evaluate the cost driver
High-Low Method
 Advantages
 Simple
 Gives quick insight in cost-activity relationships
 Disadvantages
 Uses extreme data points
 Ignores much of the data
 Steps:
 Choose high and low activity levels
 Determine the slope (VC per unit)
 Plug the slope and one data point into the cost equation
to solve for TFC
 Write the cost equation in standard form (replacing VC
and FC with the correct amounts)
 Y = VCx + FC
Regression Analysis
 A statistical technique that measures the average
amount of change in the dependent variable associated
with a unit change in the independent variable
 Excel generates output providing cost function
 Uses all the data points
 Provides a more accurate cost estimation formula than
the high-low method
Excel Regression Output
Cost equation:
Y = 177.40x + 3,121
Evaluating Cost Drivers
 Three criteria
 Economic plausibility
 Does it make sense?
 Goodness of fit
 Indicates how good a predictors of cost the equation
 R2
 Significance of independent variable
 Indicates the percentage of the total variation in the
y-values (total cost) that is explained by the
regression equation.
 Steep slope
 Indicates the strength of the relationship between
the cost driver and the costs incurred
The End