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2 M15_BERE8380_12_SE_C15.7.qxd 2/21/11 3:59 PM Page 2 2 CHAPTER 15 Multiple Regression Model Building time (purchasing sequence). Predict which product a consumer will purchase given the previous purchase of another product (purchase association). Quality and warranty management Predict the type of product that will fail during a warranty period (product failure analysis). Detect the type of individual who might be involved in fraudulent activities concerning the warranty of the product (warranty fraud) Insurance Predict the characteristics of a claim and an individual that indicate a fraudulent claim (fraud detection). Predict the characteristics of an individual who will file a specific type of claim (claim submission). Data Mining for Exploratory Data Analysis Data mining has other applications besides predictive modeling. Data mining can be an exploratory data analysis tool that allows business decision makers to examine the basic features of a data set, using tables, descriptive statistics, and graphic displays. Dashboards, so called because of their similarity to an automotive dashboard, are one example of this type of data mining. For example, the Command Center application developed by The New York Jets football organization (see reference 6), helps manages all activities in their stadium as they occur on game day. Using the Command Center, team managers can instantaneously track attendance and concession and merchandise sales, comparing those statistics with historical averages or last-game values as well as determine such things as the elapsed time between when a fan first enters a parking lot and then first enters the stadium. Statistical Methods in Data Mining Besides the regression methods of Chapters 13 and 14 and this chapter, data mining uses the following methods, also discussed in this book: Bar charts Pareto charts Multidimensional contingency tables Descriptive statistics such as the mean, median, and standard deviation Boxplots Data mining and analytics in general also use statistical methods that are beyond the scope of this introductory-level book. Those methods include the following: Classification and regression trees (CART) Chi-square automatic interaction detector (CHAID) Neural nets Cluster analysis Multidimensional scaling Classification and regression trees (CART) is an example of a decision tree (see Section 4.2) algorithm that splits the data set into groups based on the values of independent or explanatory (X ) variables. The CART algorithm goes through a search process to optimize the split for each independent or explanatory (X ) variable chosen. Often, the tree has many stages or nodes and a decision needs to be made as to how to prune (cut back) the tree. Chi-square automatic interaction detector (CHAID) also uses a decision tree (see Section 4.2) algorithm that splits the data set into groups based on the values of independent or explanatory (X ) variables. Unlike CART, CHAID allows a variable to be split into more than two categories at each node of the tree. Neural nets have the advantage of using complex nonlinear regression functions to predict a response variable. Unfortunately, this method s use of a complex nonlinear function can make the results of a neural net difficult to interpret. Cluster analysis is a dimension-free procedure that attempts to subdivide or partition a set of objects into relatively homogeneous groups. These homogenous groups are developed so that objects within a group are more alike other objects in the group than they are to objects outside the group.

3 M15_BERE8380_12_SE_C15.7.qxd 2/21/11 3:59 PM Page Analytics and Data Mining 3 Multidimensional scaling uses a measure of distance to develop a mapping of objects usually within a two-dimensional space so that the characteristics separating the objects can be interpreted. Typically, multidimensional scaling attempts to maximize the goodness of fit of the actual distance between objects with the fitted distances. FIGURE Classification and regression tree (CART) results for predicting the proportion of credit card holders who would upgrade to a premium card (see next page for discussion)

5 M15_BERE8380_12_SE_C15.7.qxd 2/21/11 3:59 PM Page Analytics and Data Mining 5 In the Partition JMP dialog box (shown below): 10. Select Upgraded in the Select Columns list and click Y, Response to enter Upgraded in the box by the Y, Response button. 11. Select Purchases and click X, Factor to enter Purchases in the box by the X, Factor button. 12. Select Extra Cards and click X, Factor to enter Extra Cards in the box by the X, Factor button. 13. Click OK. JMP opens a new DATA - Partition of Upgraded - JMP window. Click Split to split the data based on whether the cardholder has additional cards. Click Split two more times to create the analysis tree shown in Figure REFERENCES 1. Davenport, T., and J. Harris, Competing on Analytics: The New Science of Winning (Boston: Harvard Business School Press, 2007). 2. Davenport, T., J. Harris, and R. Morrison, Analytics at Work (Boston: Harvard Business School Press, 2010). 3. Goel, S., et al., Predicting Consumer Behavior with Web Search, Proceedings of the National Academy of Sciences, 107 (2010): JMP Version 9 (Cary, NC: SAS Institute, Inc., 2010). 5. Nisbet, R., J. Elder, and G. Miner, Statistical Analysis and Data Mining Applications (Burlington, MA: Academic Press, 2009). 6. Serignese, K., Business Intelligence Hits the Gridiron, Software Development Times, October 1, 2010, p Tan, P.-N., M. Steinbach, and V. Kumar, Introduction to Data Mining (Boston: Addison-Wesley, 2006).

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