1 / 18

Chapter

Chapter. 19. Predictive Analysis in Marketing Research. Understanding Prediction. Prediction: statement of what is believed will happen in the future made on the basis of past experience or prior observation. Understanding Prediction Two Approaches. Two approaches to prediction:

wyoming-snider
Download Presentation

Chapter

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related