DEMAND ESTIMATION (PART II) - CA Sri Lanka · Demand Estimation: Marketing Research Approaches...
Transcript of DEMAND ESTIMATION (PART II) - CA Sri Lanka · Demand Estimation: Marketing Research Approaches...
BEC 30325: MANAGERIAL ECONOMICS
Session 03
Dr. Sumudu Perera
DEMAND ESTIMATION (PART – II)
• Marketing Research Approaches
• Scatter Diagram
• Regression Analysis
• Simple Linear Regression Model
• Ordinary Least Squares (OLS)
Dr.Sumudu Perera 21/09/2017
2Session Outline
3Demand Estimation: Marketing
Research Approaches
Consumer Surveys
Observational Research
Consumer Clinics
Market Experiments
These approaches are usually covered extensively in marketing courses, however the most important
of these are consumer surveys and market experiments.
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Consumer surveys
These surveys require the questioning of a firm’s customers in anattempt to estimate the relationship between the demand for itsproducts and a variety of variables perceived to be for themarketing and profit planning functions.
These surveys can be conducted by simply stopping andquestioning people at shopping centre or by administeringsophisticated questionnaires to a carefully constructedrepresentative sample of consumers by trained interviewers.
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Consumer surveys continued…
Major advantages: they may provide the only information
available; they can be made as simple as possible; the
researcher can ask exactly the questions they want
Major disadvantages: consumers may be unable or
unwilling to provide reliable answers; careful and extensive
surveys can be very expensive.
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Market experiments
Market experiments include attempts made by the firm to estimate the demand for the
commodity by changing price and other determinants of the demand for the commodity in
the actual market place.
Major advantages: consumers are in a real market situation; they do not know that they
being observed; they can be conducted on a large scale to ensure the validity of results.
Major disadvantages: in order to keep cost down, the experiment may be too limited so
the outcome can be questionable; competitors could try to sabotage the experiment by
changing prices and other determinants of demand under their control; competitors can
monitor the experiment to gain very useful information about the firm would prefer not to
disclose.
7Scatter Diagram
It is two dimensional graph of plotted points in which thevertical axis represents values of the dependent variable andthe horizontal axis represents values of the independent orexplanatory variable.
The patterns of the intersecting points of variables cangraphically show relationship patterns.
Mostly, scatter diagram is used to prove or disprove cause-and-effect relationship. In the following example, it shows therelationship between advertising expenditure and its salesrevenues.
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Scatter Diagram-Example
Year X Y
1 10 44
2 9 40
3 11 42
4 12 46
5 11 48
6 12 52
7 13 54
8 13 58
9 14 56
10 15 60
Scatter Diagram
Scatter diagram shows a positive
relationship between the
relevant variables. The
relationship is approximately
linear.
This gives us a rough estimates of
the linear relationship between
the variables in the form of an
equation such as
Y= a+ b X
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Regression Analysis
Regression analysis: is a statistical technique for obtaining the
line that best fits the data points so that all researchers can
reach the same results.
Regression Line: Line of Best Fit
Regression Line: Minimizes the sum of the squared vertical
deviations (et) of each point from the regression line.
This is the method called Ordinary Least Squares (OLS).
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Purpose of Regression Analysis
Regression Analysis is Used Primarily to Model Causality and
Provide Prediction
Predict the values of a dependent (response) variable based on
values of at least one independent (explanatory) variable
Explain the effect of the independent variables on the dependent
variable
The relationship between X and Y can be shown on a scatter diagram
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Regression Analysis
In the table, Y1 refers actual or observed salesrevenue of $44 mn associated with theadvertising expenditure of $10 mn in the first yearfor which data collected.
In the following graph, Y^1 is the corresponding
sales revenue of the firm estimated from theregression line for the advertising expenditure of$10 mn in the first year.
The symbol e1 is the corresponding verticaldeviation or error of the actual sales revenueestimated from the regression line in the first year.
This can be expressed as e1= Y1- Y^1.
Year X Y
1 10 44
2 9 40
3 11 42
4 12 46
5 11 48
6 12 52
7 13 54
8 13 58
9 14 56
10 15 60
13Regression Analysis
In the graph, Y^1 is thecorresponding sales revenue of thefirm estimated from the regressionline for the advertising expenditureof $10 mn in the first year.
The symbol e1 is the correspondingvertical deviation or error of theactual sales revenue estimated fromthe regression line in the first year.This can be expressed as e1= Y1-Y^1.
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Regression Analysis
Since there are 10 observation points,we have obviously 10 verticaldeviations or error (i.e., e1 to e10). Theregression line obtained is the line thatbest fits the data points in the sensethat the sum of the squared (vertical)deviations from the line is minimum. Thismeans that each of the 10 e values is
first squared and then summed.
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Simple Regression Analysis
Now we are in a position to calculate the value of a ( the vertical intercept)
and the value of b (the slope coefficient) of the regression line.
Conduct tests of significance of parameter estimates.
Construct confidence interval for the true parameter.
Test for the overall explanatory power of the regression.
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Simple Linear Regression Model
Regression line is a straight line that describes the dependence of theaverage value of one variable on the other
ii iY X
Y Intercept SlopeCoefficient Random Error
Independent (Explanatory) Variable
Regression
Line
Dependent (Response) Variable
Ordinary Least Squares (OLS)
21/09/2017Dr.Sumudu Perera
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t t tY a bX e
ˆˆ ˆt tY a bX
ˆt t te Y Y
Model:
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Ordinary Least Squares (OLS)
Objective: Determine the slope and intercept
that minimize the sum of the squared errors.
2 2 2
1 1 1
ˆˆ ˆ( ) ( )n n n
t t t t t
t t t
e Y Y Y a bX
19Ordinary Least Squares (OLS)
Estimation Procedure