1. Chapter Fourteen 14-1

2. Statistical Analysis Procedures Statistical procedures that simultaneously analyze multiple measurements on each individual or object under study. 14-2 Key Terms & Definitions

3. Statistical Analysis 14-3 Key Terms & Definitions

4. Correlation Analysis Bivariate Techniques: Statistical methods of analyzing the relationship between variables. Independent Variable: Variable believed to affect the value of the dependent variable. 14-4 Key Terms & Definitions

5. Dependent Variable: Variable expected to be explained or caused by the independent variable. Regression Analysis: The analysis of the strength of the linear relationship between variables when one is considered the independent variable and the other is the dependent variable. Correlation Analysis 14-5 Key Terms & Definitions

6. Correlation Analysis 14-6 Key Terms & Definitions

7. The Strength of Association: The coefficient of determination (R 2 ): the percentage of the total variation in the dependent variable explained by the independent variable. Pearson Correlation: Analysis of the degree to which changes in one variable are associated with changes in another for use with metric data. Measures of Association The Concepts 14-7 Key Terms & Definitions

8. Multiple Regression Key Concepts Coefficient of Determination: Measured changes in the dependent and independent variables. Regression Coefficients: Effect of the independent variable on the dependent variable. Dummy Variables: Nominally scaled variables included in regression analysis. A procedure for predicting the level or magnitude of a (metric) dependent variable based on the levels of multiple independent variables. 14-8 Key Terms & Definitions

9. Regression Analysis 14-9 Key Terms & Definitions

10. Regression Analysis 14-10 Key Terms & Definitions

11. Uses of Regression Analysis 14-11 Key Terms & Definitions

12. Coefficient of Determination: Measure of the percentage of the variation in the dependent variable explained by variations in the independent variables. Regression Coefficients: Estimates of the effect of individual independent variables on the dependent variable. Dummy Variables: In regression analysis, a way of representing two-group or dichotomous, nominally scaled independent variables by coding one group as 0 and the other as 1. 14-12 Key Terms & Definitions Regression Analysis Measurement Applications

13. Potential Use and Interpretation Problems Collinearity: Correlation of independent variables with each other, which can bias estimates of regression coefficients. Causation: Inference that a change in one variable is responsible for (caused) an observed change in another variable. Scaling of Coefficients: A method of directly comparing the magnitudes of the regression coefficients of independent variables by scaling them in the same units or by standardizing the data. 14-13 Key Terms & Definitions

14. Standardization Process 14-14 Key Terms & Definitions

15. Cluster Analysis The general term for statistical procedures that classify objects, or people, into some number of mutually exclusive and exhaustive groups on the basis of two or more classification variables. 14-14 Key Terms & Definitions Consumers who frequently eat out but seldom eat at fast-food restaurants. People who frequently eat out and also frequently eat at fast-food restaurants. People who do not frequently eat out or frequently eat at fast-food restaurants.

16. Cluster Analysis Clustering people according to how frequently and where they eat out is a way of identifying a particular consumer base. An upscale restaurant can see where its customers fall. 14-16 Key Terms & Definitions

17. Factor Analysis A procedure for simplifying data by reducing a large set of variables to a smaller set of factors of composite variables by identifying dimensions of the data. Factor: A linear combination of variables that are correlated with each other. 14-17 Key Terms & Definitions

18. Factor Scores In factor analysis, a factor score is calculated on each factor for each subject in the data set. For example, in a factor analysis with two factors, the following equations might be used to determine factor scores: 14-18 Key Terms & Definitions

19. Factor Loading Correlation between factor scores and the original variables. Other Key Issues: Naming Factors Number of factors to Retain 14-19 Key Terms & Definitions

20. Factor Loading Naming Factors This is a somewhat subjective step. Usually a certain consistency exists among the variables that load highly on a given factor. For example, it is not surprising to see ratings “smooth ride” and “quiet ride” on the same factor. Number of Factors to Retain How many factors o you retain? A general rule of thumb is to stop factoring when additional factors no longer make sense. 14-20 Key Terms & Definitions

21. Conjoint Analysis Conjoint analysis could be used by a manufacturer of golf balls to determine the three most important features of a golf ball and to see which ball meets the most needs of both consumer and manufacturer. 14-21 Key Terms & Definitions Average driving distance 10 yards more than the golfer’s average Same as the golfer's average 10 yards less than the golfer's average Average ball life 54 holes 36 holes 18 holes Price Per Ball $2.00 $2.50 $3.00

22. Conjoint Analysis Conjoint analysis could be used by a manufacturer of golf balls to determine the three most important features of a golf ball and to see which ball meets the most needs of both consumer and manufacturer. 14-22 Key Terms & Definitions For potential purchasers, the ideal golf ball has these characteristics Average driving distance – 10 yards above average Average ball life – 54 holes Price $2.00 For the manufacturer, which is based on cost, the ideal golf ball has these characteristics: Average driving distance – 10 yards below average Average ball life – 18 holes Price - $3.00

23. Conjoint Analysis Multivariate procedure used to quantify the value consumers associate with different levels of product/service attributes or features. 14-23 Key Terms & Definitions

24. Conjoint Analysis – Estimating Utilities Utilities: The relative value of attribute levels determined through conjoint analysis. 14-24 Key Terms & Definitions

25. Conjoint Analysis – Estimating Utilities Utilities: The relative value of attribute levels determined through conjoint analysis. 14-25 Key Terms & Definitions

26. After some pre-processing, the actual data mining process includes four steps: 1.Clustering 2.Classification 3.Modeling 4.Application Data Mining Process Key Terms & Definitions 14-26

27. Step One Clustering Discovering groups and structures in the data based on selected sets of variables. Step Two Classification Applying the structure identified in the cluster analysis to another subset of data. Data Mining Process Key Terms & Definitions 14-27

28. Step Three Modeling Use regression to model the relationship or predict subgroup membership. Seeking high predictive accuracy. Step Four Application Putting your actionable data to work. Data Mining Process Key Terms & Definitions 14-28

29. 14-29 Key Terms & Definitions Regression Analysis Coefficient of Determination Regression Coefficients Dummy Variables Collinearity Causation Scaling of Coefficients Links and button are active when in “Slide Show Mode” Key Terms & Definitions Discriminant Coefficient Cluster Analysis Factor Analysis Factor Scores Factor Loading Conjoint Analysis Estimating Utilities