BEC 30325 MANAGERIAL ECONOMICS Session 03 Demand Estimation

BEC 30325: MANAGERIAL ECONOMICS Session 03 Demand Estimation (Part – II) Dr. Sumudu Perera

Session Outline 2 • Marketing Research Approaches • Scatter Diagram • Regression Analysis • Simple Linear Regression Model • Ordinary Least Squares (OLS) Dr. Sumudu Perera 9/21/16

Demand Estimation: Marketing Research Approaches 3 • 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.

Consumer surveys • These surveys require the questioning of a firm’s customers in an attempt to estimate the relationship between the demand for its products and a variety of variables perceived to be for the marketing and profit planning functions. • These surveys can be conducted by simply stopping and questioning people at shopping centre or by administering sophisticated questionnaires to a carefully constructed representative sample of consumers by trained interviewers. 4

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. 5

Market experiments 6 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.

Scatter Diagram • It is two dimensional graph of plotted points in which the vertical axis represents values of the dependent variable and the horizontal axis represents values of the independent or explanatory variable. • The patterns of the intersecting points of variables can graphically show relationship patterns. • Mostly, scatter diagram is used to prove or disprove cause-andeffect relationship. In the following example, it shows the relationship between advertising expenditure and its sales revenues. 7

Scatter Diagram-Example 8

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 9

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. 10

Purpose of Regression Analysis 11 • 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

Regression Analysis • In the table, Y 1 refers actual or observed sales revenue of $44 mn associated with the advertising expenditure of $10 mn in the first year for which data collected. • In the following graph, Y^1 is the corresponding sales revenue of the firm estimated from the regression line for the advertising expenditure of $10 mn in the first year. • The symbol e 1 is the corresponding vertical deviation or error of the actual sales revenue estimated from the regression line in the first year. This can be expressed as e 1= Y 1 - Y ^1. 12

13 Regression Analysis In the graph, Y^1 is the corresponding sales revenue of the firm estimated from the regression line for the advertising expenditure of $10 mn in the first year. The symbol e 1 is the corresponding vertical deviation or error of the actual sales revenue estimated from the regression line in the first year. This can be expressed as e 1= Y 1 - Y^1.

Regression Analysis • Since there are 10 observation points, we have obviously 10 vertical deviations or error (i. e. , e 1 to e 10). The regression line obtained is the line that best fits the data points in the sense that the sum of the squared (vertical) deviations from the line is minimum. This means that each of the 10 e values is first squared and then summed. 14

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. 15

Simple Linear Regression Model 16 Regression line is a straight line that describes the dependence of the average value of one variable on the other Slope Coefficient Y Intercept Dependent (Response) Variable Regression Line Random Error Independent (Explanatory) Variable

Ordinary Least Squares (OLS) 17 Model: Dr. Sumudu Perera 9/21/16

Ordinary Least Squares (OLS) Objective: Determine the slope and intercept that minimize the sum of the squared errors. 18

Ordinary Least Squares (OLS) Estimation Procedure 19