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Study Resources (Business Management)

  Section 6.4 Review Questions 1. What is the so-called “black-box” syndrome? 2. Why is it important to be able to explain an ANN’s model structure? 3. How does sensitivity analysis work? 4. Search the Internet to find other ANN explanation methods.     .
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  Application Case 6.4: Managing Student Retention with Predictive Modeling 1. Why is attrition one of the most important issues in higher education? 2.How can predictive analytics (ANN, SVM, and so forth) be used to better manage student retention? 3.What are the main challenges and potential solutions to the use of analytics in retention.
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  Section 7.2 Review Questions 1.What is text analytics? How does it differ from text mining? 2.What is text mining? How does it differ from data mining? 3.Why is the popularity of text mining as an analytics tool increasing? 4.What are some popular application areas of text mining?     .
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  Section 7.5 Review Questions 1.What are the main steps in the text mining process? 2.What is the reason for normalizing word frequencies? What are the common methods for normalizing word frequencies? 3.What is singular value decomposition? How is it used in text mining? 4.What are the main knowledge extraction methods from corpus?     .
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  Application Case 5.7: Predicting Customer Buying Patterns—The Target Story 1. What do you think about data mining and its implications concerning privacy? What is the threshold between knowledge discovery and privacy infringement? 2.Did Target go too far? Did they do anything illegal? What do you think they should have done? What do.
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  Section 5.3 Review Questions 1.What are the major application areas for data mining? 2.Identify at least five specific applications of data mining and list five common        characteristics of these applications. 3.What do you think is the most prominent application area for data mining? Why? 4.Can you think of other application areas for data mining.
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  Application Case 5.5: 2degrees Gets a 1275 Percent Boost in Churn Identification 1. What does 2degrees do? Why is it important for 2degrees to accurately identify churn? 2degrees is New Zealand’s fastest growing mobile telecommunications company. Customer churn (customers leaving) is an all-too-common problem in the mobile telecommunications industry. So, it makes.
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Section 6.1 Review Questions 1. Why is it important to study medical procedures? What is the value in predicting outcomes? 2. What factors do you think are the most important in better understanding and managing healthcare? Consider both managerial and clinical aspects of healthcare. 3. What would be the impact of predictive modeling.
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  Application Case 6.2: Predictive Modeling Is Powering the Power Generators 1. What are the key environmental concerns in the electric power industry? 2.What are the main application areas for predictive modeling in the electric power industry? 3.How was predictive modeling used to address a variety of problems in the electric power industry?     .
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  Application Case 6.3: Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents 1. How does sensitivity analysis shed light on the black box (i.e., neural networks)? 2.Why would someone choose to use a black-box tool like neural networks over theoretically sound, mostly transparent statistical tools like logistic regression? 3.In this case, how did.
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  6.What are the main data mining application areas? Discuss the commonalities of these areas that make them a prospect for data mining studies. 7.Why do we need a standardized data mining process? What are the most commonly used data mining processes? 8.Discuss the differences between the two most commonly used data mining.
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  Section 5.5 Review Questions 1.Identify at least three of the main data mining methods. 2.Give examples of situations in which classification would be an appropriate data mining technique. Give examples of situations in which regression would be an appropriate data mining technique.     .
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  Section 6.2 Review Questions 1.What is an ANN? 2.Explain the following terms: neuron, axon, and synapse. 3.How do weights function in an ANN? 4.What is the role of the summation and transformation function? 5.What are the most common ANN architectures? How do they differ from each other?     .
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  ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION?  ?  ? 1.Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar? 2.The performance of ANN relies heavily on the summation and transformation functions. Explain the combined effects of the summation and transformation.
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  Application Case 5.3: A Mine on Terrorist Funding 1. How can data mining be used to fight terrorism? Comment on what else can be done beyond what is covered in this short application case. 2.Do you think that, although data mining is essential for fighting terrorist cells, it also jeopardizes individuals’ rights.
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  Section 6.7 Review Questions 1.What is special about the kNN algorithm? 2.What are the advantages and disadvantages of kNN as compared to ANN and SVM? 3.What are the critical success factors for a kNN implementation? 4.What is a similarity (or distance measure)? How can it be applied to both numerical and nominal valued variables? 5.What.
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  11.Discuss the reasoning behind the assessment of classification models. 12.What is the main difference between classification and clustering? Explain using concrete examples. 13.Moving beyond the chapter discussion, where else can association be used?     .
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  Section 5.6 Review Questions 1.What are the most popular commercial data mining tools? 2.Why do you think the most popular tools are developed by statistics companies? 3.What are the most popular free data mining tools? 4.What are the main differences between commercial and free data mining software tools? 5.What would be your top five selection.
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  Section 5.4 Review Questions 1.What are the major data mining processes? 2.Why do you think the early phases (understanding of the business and understanding of the data) take the longest in data mining projects? 3.List and briefly define the phases in the CRISP-DM process. 4.What are the main data preprocessing steps? Briefly describe each.
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  ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION?  ?   Application Case 6.1: Neural Networks Are Helping to Save Lives in the Mining Industry 1. How did neural networks help save lives in the mining industry? 2.What were the challenges, the proposed solution, and the obtained results? 3. What was their implementation strategy? Why is it.
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  Section 6.6 Review Questions 1.What are the main steps and decision points in developing a SVM model? 2.How do you determine the optimal kernel type and kernel parameters? 3.Compared to ANN, what are the advantages of SVM? 4.What are the common application areas for SVM? Conduct a search on the Internet to identify popular.
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Section 7.1 Review Questions 1.What is Watson? What is special about it? 2.What technologies were used in building Watson (both hardware and software)? 3.What are the innovative characteristics of DeepQA architecture that made Watson superior?     .
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  Application Case 5.6: Data Mining Goes to Hollywood: Predicting Financial Success of Movies 1. Why is it important for Hollywood professionals to predict the financial success of movies? 2.How can data mining be used to predict the financial success of movies before the start of their production process? 3. How do you think.
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  ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS?  ? 1.Why is beer flavor important to the Coors profitability? 2.What is the objective of the neural network used at Coors? 3.Why were the results of the Coors neural network initially poor, and what was done to improve the results? 4.What benefits might Coors derive if.
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  Application Case 5.2: Harnessing Analytics to Combat Crime: Predictive Analytics Helps Memphis Police Department Pinpoint Crime and Focus Police Resources 1. How did the Memphis Police Department use data mining to better combat crime? 2.What were the challenges, the proposed solution, and the obtained results?     .
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  ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION?  ?   Application Case 5.1: Smarter Insurance: Infinity P&C Improves Customer Service and Combats Fraud with Predictive Analytics 1. How did Infinity P&C improve customer service with data mining? 2.What were the challenges, the proposed solution, and the obtained results? 3. What was their implementation strategy? Why is.
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