Chapter 24: Introduction to Data Mining – Quiz A Name ______________________
24.2.2 Recognize data mining approaches and identify terms and variable types involved
in data mining.
Use the following information for questions 1 thorough 5:
Below is a list of a few variables for which data were collected from various
telecommunication companies. On the line to the right of each variable, identify whether
it is transactional (T) or a demographic (D).
1. Type of cell phone plan. _____
2. Number of residents in household. _____
3. Subscription to international channels. _____
4. Zip code. _____
5. Monthly electricity usage. _____
24.6.5 Identify models and algorithms used to build predictive models.
Use the following information for questions 6 through 8:
Suppose data mining is employed on telecommunication company data warehouse in
order to answer the following questions. On the line to the right of each, indicate whether
these involve a classification (C) or regression (R) problem.
6. Whether or not a customer would be interested in wireless internet capabilities? ____
7. How much does a customer spend on all household communication-related
expenditures? ____
8. Whether or not a customer would be interested in flexible cable TV plans (subscribe
to different channels on different days/times)? ____
24.3.4 Identify/Articulate the goal of a data mining project.
9. 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?
24.8.1 Understand the characteristics, uses, and value of data mining in business.
10. Describe the phases of the data mining process.
24-2 Chapter 24 Introduction to Data Mining
Chapter 24: Introduction to Data Mining – Quiz A – Key
Quiz B 24-3
Chapter 24: Introduction to Data Mining – Quiz B Name ____________________
24.2.2 Recognize data mining approaches and identify terms and variable types involved
in data mining.
Use the following information for questions 1 through 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. On the line to the right, indicate whether the
variable is transactional (T) or demographic (D).
1. Amount spent on organic food products _____
2. Number of international flights taken annually _____
3. Age _____
4. Types of eco-friendly products purchased _____
5. Occupation _____
24.6.5 Identify models and algorithms used to build predictive models.
Use the following information for problems 6 through 8.
Suppose data mining is employed on supermarket chains and travel industry data
warehouse in order to answer the following questions. On the line to the right, indicate
whether these involve a classification (C) or regression (R) problem.
6. Whether or not a customer is interested in eco-friendly travel products? ____
7. How much a customer spends annually on travel related products? ____
8. How much a customer spends annually on international specialty food items? ____
24.3.4 Identify/Articulate the goal of a data mining project.
9. 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?
24.1.1 Understand the characteristics, uses, and value of data mining in business.
10. Explain how data mining differs from statistical inference.
24-4 Chapter 24 Introduction to Data Mining
Chapter 24: Introduction to Data Mining – Quiz B – Key
Quiz C 24-5
Chapter 24: Introduction to Data Mining – Quiz C – Multiple Choice
Name ______________________
24.2.2 Recognize data mining approaches and identify terms and variable types involved
in data mining.
1. In a data warehouse, which of the following variables is/are demographic?
A. Number of residents in household.
B. Gender of head of household.
C. Monthly electricity usage.
D. Both A and B.
E. All of the above.
24.2.2 Recognize data mining approaches and identify terms and variable types involved
in data mining.
2. In a data warehouse, which of the following variables is/are transactional?
A. Type of cell phone plan.
B. Zip code.
C. Household income.
D. Both A and B.
E. All of the above.
24.6.5 Identify models and algorithms used to build predictive models.
3. Suppose data mining is employed 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.
24.3.5 Identify models and algorithms used to build predictive models.
4. Suppose the goal of data mining in a data warehouse was to predict whether a
household’s telecommunication needs will increase, decrease or stay the same over
the next year. What technique is most appropriate for achieving this goal?
A. Neural network.
B. Supervised problem.
C. Tree model.
D. Nodal network.
E. None of the above.
24-6 Chapter 24 Introduction to Data Mining
24.2.2 Recognize data mining approaches and identify terms and variable types involved
in data mining.
5. In a data warehouse, which of the following variables is/are transactional?
A. Amount spent on organic food products.
B. Number of international flights taken annually.
C. Types of eco-friendly products purchased.
D. Both A and B.
E. All of the above.
24.2.2 Recognize data mining approaches and identify terms and variable types involved
in data mining.
6. In a data warehouse, which of the following variables is/are demographic?
A. Age.
B. Occupation.
C. Amount spent on organic food products.
D. Both A and B.
E. All of the above.
24.6.5 Identify models and algorithms used to build predictive models.
7. Which is considered a data mining classification 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?
D. Both B and C.
E. All of the above.
24.8.1 Understand the characteristics, uses, and value of data mining in business.
8. Which is not a phase of the data mining process?
A. Business understanding.
B. Data preparation.
C. Modeling.
D. Deployment.
E. None of the above.
Quiz C 24-7
24.7.5 Identify models and algorithms used to build predictive models.
9. Popular data mining tools inspired by models that tried to mimic the function of the
brain are known as
A. Tree models.
B. Supervised problems.
C. Neural networks.
D. Nodal network.
E. None of the above.
24.8.1 Understand the characteristics, uses, and value of data mining in business.
10. Data not used in building the model but used to evaluate the performance of the
model is known as
A. the terminal node.
B. the test set.
C. meta data.
D. the training set.
E. None of the above.
24-8 Chapter 24 Introduction to Data Mining
Chapter 24: Introduction to Data Mining – Quiz C – Key
Quiz D 24-9
Chapter 24: Introduction to Data Mining – Quiz D – Multiple Choice
Name ______________________
24.2.2 Recognize data mining approaches and identify terms and variable types involved
in data mining.
1. In a data warehouse, which of the following variable(s) is/are transactional?
A. Annual expenditures on garden supplies.
B. Number of children in household.
C. Household income.
D. Both A and B.
E. All of the above.
24.2.2 Recognize data mining approaches and identify terms and variable types involved
in data mining.
2. In a data warehouse, which of the following variable(s) is/are demographic?
A. Number of magazine subscriptions.
B. Monthly expenditures on cleaning supplies.
C. Homeowner (Yes or No).
D. Both A and B.
E. All of the above.
24.6.5 Identify models and algorithms used to build predictive models.
3. Suppose data mining is used to determine whether or not a household subscribes to
magazines about home and garden. In data mining this is referred to as what type of
problem?
A. Regression.
B. Transactional.
C. Unsupervised.
D. Classification.
E. None of the above.
24.6.5 Identify models and algorithms used to build predictive models.
4. Suppose data mining is used to determine how much a customer spends annually on
energy efficient products. In data mining this is referred to as what type of problem?
A. Regression.
B. Transactional.
C. Unsupervised.
D. Classification.
E. None of the above.
24-10 Chapter 24 Introduction to Data Mining
24.2.2 Recognize data mining approaches and identify terms and variable types involved
in data mining.
5. Information about variables, such as variable definitions as well as how and when
data were collected, is collectively called
A. superdata.
B. metadata.
C. extradata.
D. cases.
E. none of the above.
24.8.1 Understand the characteristics, uses, and value of data mining in business.
6. Data used in a supervised problem to build the predictive model is known as
A. the terminal node.
B. the test set.
C. meta data.
D. the training set.
E. none of the above.
24.3.5 Identify models and algorithms used to build predictive models.
7. Suppose the goal of data mining using in a data warehouse is 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?
A. Neural network.
B. Unsupervised problem.
C. Tree model.
D. Nodal network.
E. None of the above.
24.2.2 Recognize data mining approaches and identify terms and variable types involved
in data mining.
8. In a data warehouse, which of the following variable(s) is/are transactional?
A. Gender.
B. Homeowner (Yes or No).
C. Purchase price of a vehicle.
D. Both B and C.
E. None of the above.
Quiz D 24-11
24.6.5 Identify models and algorithms used to build predictive models.
9. Suppose data mining is used to determine whether or not a customer would purchase
a hybrid vehicle. In data mining this is referred to as what type of problem?
A. Regression.
B. Transactional.
C. Unsupervised.
D. Classification.
E. None of the above.
24.6.5 Identify models and algorithms used to build predictive models.
10. Suppose data mining is used to determine how important it is for a customer to
purchase a vehicle with a very low carbon footprint. In data mining this is referred to
as what type of problem?
A. Regression.
B. Transactional.
C. Unsupervised.
D. Classification.
E. None of the above.
24-12 Chapter 24 Introduction to Data Mining
Chapter 24: Introduction to Data Mining – Quiz D – Key