This document discusses various approaches to demand estimation in marketing research, including consumer surveys, observational research, consumer clinics, and market experiments. It then provides details on regression analysis techniques, including scatter diagrams, the regression line, ordinary least squares estimation, and tests of significance. Multiple regression analysis is also covered.

3. 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

6. Scatter Diagram Regression Analysis

8. 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

10. Ordinary Least Squares (OLS) Model:

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

12. Ordinary Least Squares (OLS) Estimation Procedure

13. Ordinary Least Squares (OLS) Estimation Example

14. Ordinary Least Squares (OLS) Estimation Example

15. Tests of Significance Standard Error of the Slope Estimate

16. Tests of Significance Example Calculation

17. Tests of Significance Example Calculation

18. Tests of Significance Calculation of the t Statistic Degrees of Freedom = (n-k) = (10-2) = 8 Critical Value at 5% level =2.306

19. Tests of Significance Decomposition of Sum of Squares Total Variation = Explained Variation + Unexplained Variation

21. Tests of Significance Coefficient of Determination

23. Tests of Significance Coefficient of Correlation

24. Multiple Regression Analysis Model:

27. Multiple Regression Analysis Adjusted Coefficient of Determination

28. Multiple Regression Analysis Analysis of Variance and F Statistic

29. 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.

31. Durbin-Watson Statistic Test for Autocorrelation If d = 2, autocorrelation is absent.

32. Steps in Demand Estimation Model Specification: Identify Variables Collect Data Specify Functional Form Estimate Function Test the Results

33. Functional Form Specifications Linear Function: Power Function: Estimation Format:

36. Chapter 4 Appendix

40. 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.

42. 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

43. Example Data

44. 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

47. 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

49. 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

51. Regression Output

52. Multiple Regression Data

54. Regression Output