Chapter Fifteen.ppt

Chapter Fifteen Overview of Other Multivariate Techniquesand Data Mining Dependence and Interdependence Techniques Dependence technique One variable is designated as the dependent variable and the rest are treated as independent variables Interdependence technique There are no dependent and independent variable designations, all variables are treated equally in a search for underlying patterns of relationships Dependence Technique –Regression Analysis Input Data Dependent variable(s) - metric Independent variable(s) - metric Primary Purpose of the Technique Ascertain the relative importance of independent variable(s) in explaining variation in the dependent variable Predict dependentvariable values for given values of the independent variable(s) Table 15.1 Overview of Multivariate Techniques Analysis of Variance ANOVA is appropriate in situations where the independent variable is set at certain specific levels (called treatments in an ANOVA context) and metric measurements of the dependent variable are obtained at each of those levels Example Table 15.2 Unit Sales Data Under Three Pricing Treatments After Only Design After Only Design EG1(R) X1 O1 EG2(R) X2 O2 EG3(R) X3 O3 ANOVA –Grocery Store Hypothesis Grocery Store Example Ho ?1 = ?2 = ?3 Ha At least one ? is different from one or more of the others Hypotheses for K Treatment groups or samples Ho ?1 = ?2 = . . . = ?k Ha At least one ? is different from one or more of the others Exhibit 15.1 SPSS Computer Output for ANOVA Analysis Bank Customer Perceptions Study Bank Customer Perceptions Study (Cont’d) Bank Customer Perceptions Study (Cont’d) Bank Customer Perceptions Study (Cont’d) Factorial Anova The Factorial ANOVA is used to analyze data from a factorial design experiment variable Using the grocery store example Add in the impact of an in-store, point of purchase display on orange juice In addition to the pricing factor Exhibit 15.2 Illustrations of M