Download Business Statistics: Chapter 24: Introduction to Data Mining – Quiz A

302
CHAPTER 24
Quiz A Business Statistics
Business Statistics: Chapter 24: Introduction to Data Mining – Quiz A
Name ________________________________________
1. Data gathered from various telecommunication companies (e.g. cable, phone, internet
service providers) and utility companies (electric, fuel, etc.) were merged into one data
warehouse. Below is a list of a few variables for which data were collected. Indicate
whether these data are transactional or demographic.
a.
b.
c.
d.
e.
Type of cell phone plan.
Number of residents in household.
Subscription to international channels.
Zip code.
Monthly electricity usage.
2. Suppose data mining is employed on this data warehouse in order to answer the
following questions. Indicate whether these involve a classification or regression
problem.
a. Whether or not a customer would be interested in wireless internet capabilities?
b. How much does a customer spend on all household communication-related
expenditures?
c. Whether or not a customer would be interested in flexible cable TV plans (subscribe to
different channels on different days/times)?
3. Suppose the goal of data mining using this data warehouse was to predict whether a
household’s telecommunication needs will increase, decrease or stay the same over the
next year. What technique might be most appropriate for achieving this goal?
4. Describe the phases of the data mining process.
Copyright © 2010 Pearson Education, Inc. Publishing as Addison-Wesley.
CHAPTER 24
Quiz A Business Statistics 303
Business Statistics: Chapter 24: Introduction to Data Mining – Quiz A – Key
1. Data gathered from various telecommunication companies (e.g. cable, phone, internet
service providers) and utility companies (electric, fuel, etc.) were merged into one data
warehouse. Below is a list of a few variables for which data were collected. Indicate
whether these data are transactional or demographic.
a.
b.
c.
d.
e.
Type of cell phone plan. Transactional.
Number of residents in household. Demographic.
Subscription to international channels. Transactional.
Zip code. Demographic.
Monthly electricity usage. Transactional.
2. Suppose data mining is employed on this data warehouse in order to answer the
following questions. Indicate whether these involve a classification or regression
problem.
a. Whether or not a customer would be interested in wireless internet capabilities?
Classification.
b. How much does a customer spend on all household communication-related
expenditures?
Regression.
c. Whether or not a customer would be interested in flexible cable TV plans (subscribe to
different channels on different days/times)?
Classification.
3. Suppose the goal of data mining using this data warehouse was to predict whether a
household’s telecommunication needs will increase, decrease or stay the same over the
next year. What technique might be most appropriate for achieving this goal?
Tree model.
4. Describe the phases of the data mining process.
Business understanding, data understanding, data preparation, modeling, evaluation of
models using test data set, and deployment.
Copyright © 2010 Pearson Education, Inc. Publishing as Addison-Wesley.
304
CHAPTER 24
Quiz B Business Statistics
Business Statistics: Chapter 24: Introduction to Data Mining – Quiz B
Name ________________________________________
1. Scanner data gathered from various supermarket chains were merged with data from
the travel industry (e.g., airlines, hotels, etc) into one data warehouse. Below is a list of a
few variables for which data were collected. Indicate whether these data are transactional
or demographic.
a.
b.
c.
d.
e.
Amount spent on organic food products.
Number of international flights taken annually.
Age.
Types of eco-friendly products purchased.
Occupation.
2. Suppose data mining is employed on this data warehouse in order to answer the
following questions. Indicate whether these involve a classification or regression
problem.
a. Whether or not a customer is interested in eco-friendly travel products?
b. How much a customer spends annually on travel related products?
c. How much a customer spends annually on international specialty food items?
3. Suppose the goal of data mining using this data warehouse was to predict whether a
customer’s expenditures on international specialty food items would increase, decrease or
stay the same in the next year? What technique might be most appropriate for achieving
this goal?
4. Explain how data mining differs from statistical inference.
Copyright © 2010 Pearson Education, Inc. Publishing as Addison-Wesley.
CHAPTER 24
Quiz B Business Statistics 305
Business Statistics: Chapter 24: Introduction to Data Mining – Quiz B – Key
1. Scanner data gathered from various supermarket chains were merged with data from
the travel industry (e.g., airlines, hotels, etc) into one data warehouse. Below is a list of a
few variables for which data were collected. Indicate whether these data are transactional
or demographic.
a.
b.
c.
d.
e.
Amount spent on organic food products. Transactional.
Number of international flights taken annually. Transactional.
Age. Demographic.
Types of eco-friendly products purchased. Transactional.
Occupation. Demographic.
2. Suppose data mining is employed on this data warehouse in order to answer the
following questions. Indicate whether these involve a classification or regression
problem.
a. Whether or not a customer is interested in eco-friendly travel products?
Classification.
b. How much a customer spends annually on travel related products?
Regression.
c. How much a customer spends annually on international specialty food items?
Regression.
3. Suppose the goal of data mining using this data warehouse was to predict whether a
customer’s expenditures on international specialty food items would increase, decrease or
stay the same in the next year? What technique might be most appropriate for achieving
this goal?
Tree Model.
4. Explain how data mining differs from statistical inference.
Size of the databases is much larger; data mining is exploratory by nature; data are
happenstance rather than collected from planned designed studies; modeling choices are
automatic.
Copyright © 2010 Pearson Education, Inc. Publishing as Addison-Wesley.
306
CHAPTER 24
Quiz C Business Statistics
Business Statistics: Chapter 24: Introduction to Data Mining – Quiz C
1. Data gathered from various telecommunication companies (e.g. cable, phone,
internet service providers) and utility companies (electric, fuel, etc.) were merged into
one data warehouse. Below are some of the variables for which data were collected.
Which is/are demographic?
A.
B.
C.
D.
E.
Number of residents in household.
Gender of head of household.
Monthly electricity usage.
Both A and B.
All of the above.
2. Additional variables for which data were collected in the telecommunication
companies’ data warehouse are shown below. Which is/are transactional?
A.
B.
C.
D.
E.
Type of cell phone plan.
Zip code.
Household income.
Both A and B.
All of the above.
3. Suppose data mining is employed on this telecommunication companies’ data
warehouse in order to answer the following questions. Which is considered a regression
problem?
A. Whether or not a customer would be interested in wireless internet capabilities?
B. How much does a customer spend on all household communication-related
expenditures?
C. Whether or not a customer would be interested in flexible cable TV plans (subscribe
to different channels on different days/times)?
D. Both A and B.
E. All of the above.
4. Suppose the goal of data mining using this data warehouse was to predict whether a
household’s telecommunication needs will increase, decrease or stay the same over the
next year. What technique might be most appropriate for achieving this goal?
A.
B.
C.
D.
E.
Neural network.
Supervised problem.
Tree model.
Nodal network.
None of the above.
Copyright © 2010 Pearson Education, Inc. Publishing as Addison-Wesley.
CHAPTER 24
Quiz C Business Statistics 307
5. Scanner data gathered from various supermarket chains were merged with data from
the travel industry (e.g., airlines, hotels, etc) into one data warehouse. Below is a list of a
few variables for which data were collected. Which is/are transactional?
A.
B.
C.
D.
E.
Amount spent on organic food products.
Number of international flights taken annually.
Types of eco-friendly products purchased.
Both A and B.
All of the above.
6. Additional variables for which data were collected in the grocery – travel data
warehouse are shown below. Which is/are demographic?
A.
B.
C.
D.
E.
Age.
Occupation.
Amount spent on organic food products.
Both A and B.
All of the above.
7. Suppose data mining is employed on this grocery – travel data warehouse in order to
answer the following questions. Which is considered a classification problem?
A.
B.
C.
D.
E.
Whether or not a customer is interested in eco-friendly travel products?
How much a customer spends annually on travel related products?
How much a customer spends annually on international specialty food items?
Both B and C.
All of the above.
8. Which is not a phase of the data mining process?
A.
B.
C.
D.
E.
Business understanding.
Data preparation.
Modeling.
Deployment.
None of the above.
9. Popular data mining tools inspired by models that tried to mimic the function of the
brain are known as
A.
B.
C.
D.
E.
Tree models.
Supervised problems.
Neural networks.
Nodal network.
None of the above.
Copyright © 2010 Pearson Education, Inc. Publishing as Addison-Wesley.
308
CHAPTER 24
Quiz C Business Statistics
10. Data not used in building the model but used to evaluate the performance of the
model is known as
A.
B.
C.
D.
E.
the terminal node.
the test set.
meta data.
the training set.
None of the above.
Business Statistics: Chapter 24: Introduction to Data Mining – Quiz C – Key
1. D
2. A
3. B
4. C
5. E
6. D
7. A
8. E
9. C
10. B
Copyright © 2010 Pearson Education, Inc. Publishing as Addison-Wesley.