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Ch. 4-demand-estimation(2)
 

Ch. 4-demand-estimation(2)

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Microeconomics-Salvatore

Microeconomics-Salvatore

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    Ch. 4-demand-estimation(2) Ch. 4-demand-estimation(2) Presentation Transcript

    • Marketing Research Approaches to Demand Estimation
      Consumer Surveys
      data from survey questions
      Observational Research
      data from observed behavior
      Consumer Clinics
      data from laboratory experiments
      Market Experiments
      data from real market tests
    • Scatter Diagram
      Regression Analysis
    • Regression Analysis
      Regression Line: Line of Best Fit
      Regression Line: Minimizes the sum of the squared vertical deviations (et) of each point from the regression line.
      Ordinary Least Squares (OLS) Method
    • Ordinary Least Squares (OLS)
      Model:
    • Ordinary Least Squares (OLS)
      Objective: Determine the slope and intercept that minimize the sum of the squared errors.
    • Ordinary Least Squares (OLS)
      Estimation Procedure
    • Ordinary Least Squares (OLS)
      Estimation Example
    • Ordinary Least Squares (OLS)
      Estimation Example
    • Tests of Significance
      Standard Error of the Slope Estimate
    • Tests of Significance
      Example Calculation
    • Tests of Significance
      Example Calculation
    • Tests of Significance
      Calculation of the t Statistic
      Degrees of Freedom = (n-k) = (10-2) = 8
      Critical Value at 5% level =2.306
    • Tests of Significance
      Decomposition of Sum of Squares
      Total Variation = Explained Variation + Unexplained Variation
    • Tests of Significance
      Coefficient of Determination
    • Tests of Significance
      Coefficient of Correlation
    • Multiple Regression Analysis
      Model:
    • Multiple Regression Analysis
      Adjusted Coefficient of Determination
    • Multiple Regression Analysis
      Analysis of Variance and F Statistic
    • Problems in Regression Analysis
      Multicollinearity: Two or more explanatory variables are highly correlated.
      Heteroskedasticity: Variance of error term is not independent of the Y variable.
      Autocorrelation: Consecutive error terms are correlated.
    • Durbin-Watson Statistic
      Test for Autocorrelation
      If d = 2, autocorrelation is absent.
    • Steps in Demand Estimation
      Model Specification: Identify Variables
      Collect Data
      Specify Functional Form
      Estimate Function
      Test the Results
    • Functional Form Specifications
      Linear Function:
      Power Function:
      Estimation Format:
    • Chapter 4 Appendix
    • Getting Started
      Install the Analysis ToolPak add-in from the Excel installation media if it has not already been installed
      Attach the Analysis ToolPak add-in
      From the menu, select Tools and then Add-Ins...
      When the Add-Ins dialog appears, select Analysis ToolPak and then click OK.
    • Entering Data
      Data on each variable must be entered in a separate column
      Label the top of each column with a symbol or brief description to identify the variable
      Multiple regression analysis requires that all data on independent variables be in adjacent columns
    • Example Data
    • Running the Regression
      Select the Regression tool from the Analysis ToolPak dialog
      From the menu, select Tools and then Data Analysis...
      On the Data Analysis dialog, scroll down the list of Analysis Tools, select Regression, and then click OK
      The Regression tool dialog will then be displayed
    • Select the Data Ranges
      Type in the data range for the Y variable or select the range on the worksheet
      Type in the data range for the X variable(s) or select the range on the worksheet
      If your ranges include the data labels (recommended) then check the labels option
    • Select an Output Option
      Output to a selected range
      Selection is the upper left corner of the output range
      Output to a new worksheet
      Optionally enter a name for the worksheet
      Output to a new workbook
      And then click OK
    • Regression Output
    • Multiple Regression Data
    • Regression Output