Predictive Analysis in Marketing Research 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: Extrapolation: detects a pattern in the past and projects it into the future Predictive model: uses relationships among variables to make a prediction

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).

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

Bivariate Regression Analysis

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

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

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

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

Multiple Regression Analysis

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

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

Multiple Regression Analysis

Multiple Regression Analysis

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.

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.

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.