Chapter

1. Chapter 19 Predictive Analysis in Marketing Research

2. Understanding Prediction • Prediction: statement of what is believed will happen in the future made on the basis of past experience or prior observation

3. Understanding Prediction Two Approaches • Two approaches to prediction: • Extrapolation: detects a pattern in the past and projects it into the future • Predictive model: uses relationships among variables to make a prediction

4. Understanding Prediction Goodness of Predictions • All predictions should be judges as to their “goodness” (accuracy). • The goodness of a predictions is based on examination of the residuals (errors: comparisons of predictions to actual values).

5. Bivariate Regression Analysis • With bivariate analysis, one variable is used to predict another variable. • The straight-line equation is the basis of regression analysis.

6. Bivariate Regression Analysis

7. Bivariate Regression Analysis Basic Procedure • Independent variable: used to predict the independent variable (x in the regression straight-line equation) • Dependent variable: that which is predicted (y in the regression straight-line equation) • Least squares criterion: used in regression analysis; guarantees that the “best” straight-line slope and intercept will be calculated

8. Bivariate Regression Analysis Basic Procedure…cont. • The regression model, intercept, and slope must always be tested for statistical significance. • Regression analysis predictions are estimates that have some amount of error in them. • Standard error of the estimate: used to calculate a range of the prediction made with a regression equation

9. Bivariate Regression Analysis Basic Procedure…cont. • Regression predictions are made with confidence intervals

10. Multiple Regression Analysis • Multiple regression analysis uses the same concepts as bivariate regression analysis, but uses more than one independent variable. • General conceptual model: identifies independent and dependent variables and shows their basic relationships to one another

11. Multiple Regression Analysis

12. Multiple Regression Analysis • Multiple regression: means that you have more than one independent variable to predict a single dependent variable

13. Multiple Regression Analysis • Basic assumptions: • A regression plane is used instead of a line. • Coefficient of determination (multiple R): indicates how well the independent variables can predict the dependent variable in multiple regression • Independence assumption: the independent variables must be statistically independent and uncorrelated with one another • Variance inflation factor (VIF): can be used to assess and eliminate multicollinearity

14. Multiple Regression Analysis

15. Multiple Regression Analysis

16. Multiple Regression Analysis • Special uses of multiple regression: • Dummy independent variable: scales with a nominal 0-versus-1 coding scheme • Standardized beta coefficient: betas that indicate the relative importance of alternative predictor variables • Multiple regression is sometimes used to help a marketer apply market segmentation.

17. Stepwise Multiple Regression • Stepwise regression is useful when there are many independent variables, and a researcher wants to narrow the set down to a smaller number of statistically significant variables. • The one independent variable that is statistically significant and explains the most variance is entered into the multiple regression equation. • Then each statistically significant independent variable is added in order of variance explained. • All insignificant independent variable are eliminated.

18. Two Warnings Regarding Multiple Regression Analysis • Regression is a statistical tool, not a cause-and-effect statement. • Regression analysis should not be applied outside the boundaries of data used to develop the regression model.