Business Intelligence, 4e Sharda/Delen/Turban) Chapter4 Predictive Analytics I: Data Mining Process, Methods, and algorithms 1)In the opening case, police detectives used data mining to identify possible new areas of Answer FALSE Diff: 1 Page Ref: 190-191 2) The cost of data storage has plummeted recently, making data mining feasible for more firms Answer TRUE Diff: 2 Page Ref: 194 3)Data mining can be very useful in detecting patterns such as cred it card fraud, but is of little help in improving sales Answer: FALSE Diff: 2 Page Ref: 193 4)If using a mining analogy, knowledge mining"would be a more appropriate term than"data mining Answer: TRUE Diff: 2 Page Ref: 19 5)The entire focus of the predictive analytics system in the Infinity P&c case was on detecting and handling fraudulent claims for the company's benefit Answer: FALsE Diff: 3 Page Ref: 194-195 6) Data mining requires specialized data analysts to ask ad hoc questions and obtain answers quickly from the system Answer: FALSE Diff: 2 Page Ref: 197 7)Ratio data is a type of categorical data Answer FALSE Diff: 1 Page Ref: 202 8)Converting continuous valued numerical variables to ranges and categories is referred to as d iscretization Answer: TRU Diff: 2 Page Ref: 202 9)In the Miami-Dade Police Department case study, predictive analytics helped to identify the best schedule for officers in order to pay the least overtime Answer: FALSE Diff: 1 Page Ref: 190-191 Copyright C 2018 Pearson Education, Inc
1 Copyright © 2018 Pearson Education, Inc. Business Intelligence, 4e (Sharda/Delen/Turban) Chapter 4 Predictive Analytics I: Data Mining Process, Methods, and Algorithms 1) In the opening case, police detectives used data mining to identify possible new areas of inquiry. Answer: FALSE Diff: 1 Page Ref: 190-191 2) The cost of data storage has plummeted recently, making data mining feasible for more firms. Answer: TRUE Diff: 2 Page Ref: 194 3) Data mining can be very useful in detecting patterns such as credit card fraud, but is of little help in improving sales. Answer: FALSE Diff: 2 Page Ref: 193 4) If using a mining analogy, "knowledge mining" would be a more appropriate term than "data mining." Answer: TRUE Diff: 2 Page Ref: 196 5) The entire focus of the predictive analytics system in the Infinity P&C case was on detecting and handling fraudulent claims for the company's benefit. Answer: FALSE Diff: 3 Page Ref: 194-195 6) Data mining requires specialized data analysts to ask ad hoc questions and obtain answers quickly from the system. Answer: FALSE Diff: 2 Page Ref: 197 7) Ratio data is a type of categorical data. Answer: FALSE Diff: 1 Page Ref: 202 8) Converting continuous valued numerical variables to ranges and categories is referred to as discretization. Answer: TRUE Diff: 2 Page Ref: 202 9) In the Miami-Dade Police Department case study, predictive analytics helped to identify the best schedule for officers in order to pay the least overtime. Answer: FALSE Diff: 1 Page Ref: 190-191
10)In data mining, classification models help in pred iction Answer TRUE Diff: 2 Page Ref: 215 11)Statistics and data mining both look for data sets that are as large as possible Answer FALSE Diff: 2 Page Ref: 216 12)Using data mining on data about imports and exports can help to detect tax avoidance and laund ering Answer TRUE Diff: 1 Page Ref: 206 13)In the cancer research case study, data mining algorithms that predict cancer survivability with high predictive power are good replacements for medical professionals Answer: FALsE Diff: 2 Page Ref: 209-210 14)During classification in data mining, a false positive is an occurrence classified as true by the algorithm while being false in reality Answer: TRUE Diff: 2 Page Ref: 216 15)K-fold cross-validation is also called slid ing estimation Answer: FALSE Diff: 2 Page ref: 218 16 When a problem has many attributes that impact the classification of different patterns decision trees may be a useful approach Answer: TRUE Diff: 2 Page Ref: 221 17) In the Dell cases study, the largest issue was how to properly spend the online marketing budget Answer FALSE Diff: 2 Page Ref: 198-199 18 )Market basket analysis is a useful and entertaining way to explain data mining to a technologically less savvy audience but it has little business signific Answer: FALSE Dift Page Ref: 227 19)Open-source data mining tools include applications such as IBM SPSS Modeler and Dell Statistica Answer: FALSE Diff: 1 Page Ref: 231 Copyright C 2018 Pearson Education, Inc
2 Copyright © 2018 Pearson Education, Inc. 10) In data mining, classification models help in prediction. Answer: TRUE Diff: 2 Page Ref: 215 11) Statistics and data mining both look for data sets that are as large as possible. Answer: FALSE Diff: 2 Page Ref: 216 12) Using data mining on data about imports and exports can help to detect tax avoidance and money laundering. Answer: TRUE Diff: 1 Page Ref: 206 13) In the cancer research case study, data mining algorithms that predict cancer survivability with high predictive power are good replacements for medical professionals. Answer: FALSE Diff: 2 Page Ref: 209-210 14) During classification in data mining, a false positive is an occurrence classified as true by the algorithm while being false in reality. Answer: TRUE Diff: 2 Page Ref: 216 15) K-fold cross-validation is also called sliding estimation. Answer: FALSE Diff: 2 Page Ref: 218 16) When a problem has many attributes that impact the classification of different patterns, decision trees may be a useful approach. Answer: TRUE Diff: 2 Page Ref: 221 17) In the Dell cases study, the largest issue was how to properly spend the online marketing budget. Answer: FALSE Diff: 2 Page Ref: 198-199 18) Market basket analysis is a useful and entertaining way to explain data mining to a technologically less savvy audience, but it has little business significance. Answer: FALSE Diff: 2 Page Ref: 227 19) Open-source data mining tools include applications such as IBM SPSS Modeler and Dell Statistica. Answer: FALSE Diff: 1 Page Ref: 231
20) Data that is collected, stored, and analyzed in data mining is often private and personal There is no way to maintain individuals' privacy other than being very careful about physical data security Answer: FALsE Diff: 2 Page Ref: 237 21)In the Influence Health case study, what was the goal of the system? A)locating clinic patients B)understanding follow-up care C)decreasing operational costs D)increasing service use Answer: D Diff: 3 Page Ref: 224 22)Understand ing customers better has helped Amazon and others become more successful. The understand ing comes primarily from A)collecting data about customers and transactions B)developing a philosophy that is data analytics-centric C)analyzing the vast data amounts routinely collected D)asking the customers what they want A Diff: 3 Page Ref: 193 23)All of the following statements about data mining are true EXCEPT A)the process aspect means that data mining should be a one-step process to results B)the novel aspect means that previously unknown patterns are discovered C)the potentially useful aspect means that results should lead to some business benefit D) the valid aspect means that the discovered patterns should hold true on new data Answer A Diff: 3 Page Ref: 196 24)What is the main reason parallel processing is sometimes used for data mining a) because the hard ware exists in most organizations, and it is available to use B)because most of the algorithms used for data mining require it C)because of the massive data amounts and search efforts involved D) because any strategic application requires parallel processing Answer: C Diff: 3 Page Ref: 197 25)The data field"ethnic group"can be best described as B)interval data C)ordinal data D)ratio data ansy Diff: 2 Page Ref: 208 Copyright C 2018 Pearson Education, Inc
3 Copyright © 2018 Pearson Education, Inc. 20) Data that is collected, stored, and analyzed in data mining is often private and personal. There is no way to maintain individuals' privacy other than being very careful about physical data security. Answer: FALSE Diff: 2 Page Ref: 237 21) In the Influence Health case study, what was the goal of the system? A) locating clinic patients B) understanding follow-up care C) decreasing operational costs D) increasing service use Answer: D Diff: 3 Page Ref: 224 22) Understanding customers better has helped Amazon and others become more successful. The understanding comes primarily from A) collecting data about customers and transactions. B) developing a philosophy that is data analytics-centric. C) analyzing the vast data amounts routinely collected. D) asking the customers what they want. Answer: C Diff: 3 Page Ref: 193 23) All of the following statements about data mining are true EXCEPT A) the process aspect means that data mining should be a one-step process to results. B) the novel aspect means that previously unknown patterns are discovered. C) the potentially useful aspect means that results should lead to some business benefit. D) the valid aspect means that the discovered patterns should hold true on new data. Answer: A Diff: 3 Page Ref: 196 24) What is the main reason parallel processing is sometimes used for data mining? A) because the hardware exists in most organizations, and it is available to use B) because most of the algorithms used for data mining require it C) because of the massive data amounts and search efforts involved D) because any strategic application requires parallel processing Answer: C Diff: 3 Page Ref: 197 25) The data field "ethnic group" can be best described as A) nominal data. B) interval data. C) ordinal data. D) ratio data. Answer: A Diff: 2 Page Ref: 208
26)A data mining study is specific to addressing a well-defined business task, and different business tasks require A)general organizational data B)general industry data C)general economic data D)different sets of data Answer: D Diff: 2 Page Ref: 208 27)Which broad area of data mining applications analyzes data, forming rules to distinguish between defined classes? A)associations B)visualization C)classification D)clustering Answer: C Diff: 2 Page Ref: 200 28)Which broad area of data mining applications partitions a collection of objects into natural groupings with similar features? A)associations B)visualization C)classification D)clustering Answer: D Diff: 2 Page Ref: 200 29)Clustering partitions a collection of things into segments whose members share A)similar characteristics B)dissimilar characteristics C)similar collection methods D)dissimilar collection methods Answer: A Diff: 2 Page Ref: 202 30)Identifying and preventing incorrect claim payments and fraudulent activities falls under which type of data mining applications? A)insurance B)retailing and logistic C)customer relationship management D)computer hardware and software Answer: A Diff: 2 Page ref: 204 Copyright C 2018 Pearson Education, Inc
4 Copyright © 2018 Pearson Education, Inc. 26) A data mining study is specific to addressing a well-defined business task, and different business tasks require A) general organizational data. B) general industry data. C) general economic data. D) different sets of data. Answer: D Diff: 2 Page Ref: 208 27) Which broad area of data mining applications analyzes data, forming rules to distinguish between defined classes? A) associations B) visualization C) classification D) clustering Answer: C Diff: 2 Page Ref: 200 28) Which broad area of data mining applications partitions a collection of objects into natural groupings with similar features? A) associations B) visualization C) classification D) clustering Answer: D Diff: 2 Page Ref: 200 29) Clustering partitions a collection of things into segments whose members share A) similar characteristics. B) dissimilar characteristics. C) similar collection methods. D) dissimilar collection methods. Answer: A Diff: 2 Page Ref: 202 30) Identifying and preventing incorrect claim payments and fraudulent activities falls under which type of data mining applications? A) insurance B) retailing and logistics C) customer relationship management D) computer hardware and software Answer: A Diff: 2 Page Ref: 204
31)All of the following statements about data mining are true EXCEPt A)The term is relatively new B)Its techniques have their roots in trad itional statistical analysis and artificial intelligence C)The ideas behind it are relatively new D)Intense, global competition make its application more important A nswer Diff: 2 Page Ref: 194 A)SEMMA B)proprietary organizational methodologies C)KDD Process D) CRISP-DM A nswer Diff: 2 Page Ref: 214 33)Prediction problems where the variables have numeric values are most accurately defined as A)classificati B)regressions D)computations Al nswer Diff: 3 Page Ref: 215 34)What does the robustness of a data mining method refer to? A)its ability to predict the outcome of a previously unknown data set accurately B)its speed of computation and computational costs in using the mode C)its ability to construct a prediction model efficiently given a large amount of data D) its ability to overcome noisy data to make somewhat accurate predictions Diff: 3 Page Ref: 216 35)What does the scalability of a data mining method refer to? A)its ability to predict the outcome of a previously unknown data set accurately B)its speed of computation and computational costs in using the mode C)its ability to construct a pred iction model efficiently given a large amount of data D)its ability to overcome noisy data to make somewhat accurate predictions Answer: C Diff: 3 Page Ref: 216 Copyright C 2018 Pearson Education, Inc
5 Copyright © 2018 Pearson Education, Inc. 31) All of the following statements about data mining are true EXCEPT: A) The term is relatively new. B) Its techniques have their roots in traditional statistical analysis and artificial intelligence. C) The ideas behind it are relatively new. D) Intense, global competition make its application more important. Answer: C Diff: 2 Page Ref: 194 32) Which data mining process/methodology is thought to be the most comprehensive, according to kdnuggets.com rankings? A) SEMMA B) proprietary organizational methodologies C) KDD Process D) CRISP-DM Answer: D Diff: 2 Page Ref: 214 33) Prediction problems where the variables have numeric values are most accurately defined as A) classifications. B) regressions. C) associations. D) computations. Answer: B Diff: 3 Page Ref: 215 34) What does the robustness of a data mining method refer to? A) its ability to predict the outcome of a previously unknown data set accurately B) its speed of computation and computational costs in using the mode C) its ability to construct a prediction model efficiently given a large amount of data D) its ability to overcome noisy data to make somewhat accurate predictions Answer: D Diff: 3 Page Ref: 216 35) What does the scalability of a data mining method refer to? A) its ability to predict the outcome of a previously unknown data set accurately B) its speed of computation and computational costs in using the mode C) its ability to construct a prediction model efficiently given a large amount of data D) its ability to overcome noisy data to make somewhat accurate predictions Answer: C Diff: 3 Page Ref: 216