Test Bank for Statistics for Managers Using Microsoft Excel, 9th Edition

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TEST BANK
J AMES B ALDONE
Birmingham Southern College
S TATISTICS FOR M ANAGERS
U SING M ICROSOFT ® E XCEL ®
N INTH EDITION
David M. Levine
Department of Information Systems and Statistics
Zicklin School of Business, Baruch College, City University of New York
David F. Stephan
Two Bridges Instructional Technology
Kathryn A. Szabat
Department of Business Systems and Analytics
School of Business, La Salle University

iii
CONTENTS
Getting Started: Important Things to Learn First .................................................................. 0-1
Chapter 1: Defining and Collecting Data............................................................................... 1-1
Chapter 2: Organizing and Visualizing Variables ................................................................. 2-1
Chapter 3: Numerical Descriptive Measures ......................................................................... 3-1
Chapter 4: Basic Probability .................................................................................................. 4-1
Chapter 5: Discrete Probability Distributions ........................................................................ 5-1
Chapter 6: The Normal Distribution and Other Continuous Distributions ............................ 6-1
Chapter 7: Sampling Distributions......................................................................................... 7-1
Chapter 8: Confidence Interval Estimation............................................................................ 8-1
Chapter 9: Fundamentals of Hypothesis Testing: One-Sample Tests ................................... 9-1
Chapter 10: Two-Sample Tests............................................................................................ 10-1
Chapter 11: Analysis of Variance ........................................................................................ 11-1
Chapter 12: Chi-Square and Nonparametric Tests .............................................................. 12-1
Chapter 13: Simple Linear Regression ................................................................................ 13-1
Chapter 14: Introduction to Multiple Regression ................................................................ 14-1
Chapter 15: Multiple Regression Model Building ............................................................... 15-1
Chapter 16: Time-Series Forecasting ................................................................................... 16-1
Chapter 17: Business Analytics ........................................................................................... 17-1
Chapter 18: A Roadmap for Analyzing Data ....................................................................... 18-1
Chapter 19: Statistical Applications in Quality Management (ONLINE) ........................... 19-1
Chapter 20: Decision Making (ONLINE)............................................................................ 20-1

0-1
Getting Started: Important Things to Learn First
1. The process of using data collected from a small group to reach conclusions about a large
group is called
a) statistical inference.
b) DCOVA framework.
c) operational definition.
d) descriptive statistics.
ANSWER:
a
TYPE: MC DIFFICULTY: Easy
KEYWORDS: inferential statistics
2. Those methods involving the collection, presentation, and characterization of a set of data
in order to properly describe the various features of that set of data are called
a) statistical inference.
b) DCOVA framework.
c) operational definition.
d) descriptive statistics.
ANSWER:
d
TYPE: MC DIFFICULTY: Easy
KEYWORDS: descriptive statistics
3. The collection and summarization of the socioeconomic and physical characteristics of the
employees of a particular firm are examples of
a) inferential statistics.
b) descriptive statistics.
c) operational definition.
d) DCOVA framework.
ANSWER:
b
TYPE: MC DIFFICULTY: Easy
KEYWORDS: descriptive statistics
4. The estimation of the population average family expenditure on food based on the sample
average expenditure of 1,000 families is an example of
a) inferential statistics.
b) descriptive statistics.
c) DCOVA framework.
d) operational definition.
ANSWER:
a

0-2 Getting Started: Important Things to Learn First
TYPE: MC DIFFICULTY: Easy
KEYWORDS: inferential statistics
5. Which of the following is not an element of descriptive statistical problems?
a) An inference made about the population based on the sample.
b) The population or sample of interest.
c) Tables, graphs, or numerical summary tools.
d) Identification of patterns in the data.
ANSWER:
a
TYPE: MC DIFFICULTY: Moderate
KEYWORDS: descriptive statistics
6. A study is under way in Yosemite National Forest to determine the adult height of
American pine trees. Specifically, the study is attempting to determine what factors aid a
tree in reaching heights greater than 60 feet tall. It is estimated that the forest contains
25,000 adult American pines. The study involves collecting heights from 250 randomly
selected adult American pine trees and analyzing the results. Identify the variable of
interest in the study.
a) The age of an American pine tree in Yosemite National Forest.
b) The height of an American pine tree in Yosemite National Forest.
c) The number of American pine trees in Yosemite National Forest.
d) The species of trees in Yosemite National Forest.
ANSWER:
b
TYPE: MC DIFFICULTY: Easy
KEYWORDS: variable
7. Most analysts focus on the cost of tuition as the way to measure the cost of a college
education. But incidentals, such as textbook costs, are rarely considered. A researcher at
Drummand University wishes to estimate the textbook costs of first-year students at
Drummand. To do so, she monitored the textbook cost of 250 first-year students and found
that their average textbook cost was $600 per semester. Identify the variable of interest to
the researcher.
a) The textbook cost of first-year Drummand University students.
b) The year in school of Drummand University students.
c) The age of Drummand University students.
d) The cost of incidental expenses of Drummand University students.
ANSWER:
a
TYPE: MC DIFFICULTY: Easy
KEYWORDS: variable

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Getting Started: Important Things to Learn First 0-3
8. True or False: Problems may arise when statistically unsophisticated users who do not
understand the assumptions behind the statistical procedures or their limitations are misled
by results obtained from computer software.
ANSWER:
True
TYPE: TF DIFFICULTY: Easy
KEYWORDS: statistical package
9. True or False: Managers need an understanding of statistics to be able to present and
describe information accurately, draw conclusions about large populations based on small
samples, improve processes, and make reliable forecasts.
ANSWER:
True
TYPE: TF DIFFICULTY: Easy
KEYWORDS: reasons for learning statistics
10. True or False: A professor computed the sample average exam score of 20 students and
used it to estimate the average exam score of the 1,500 students taking the exam. This is an
example of inferential statistics.
ANSWER:
True
TYPE: TF DIFFICULTY: Easy
KEYWORDS: descriptive statistics, inferential statistics
11. True or False: Using the number of registered voters who turned out to vote for the primary
in Iowa to predict the number of registered voters who will turn out to vote in Vermont’s
primary is an example of descriptive statistics.
ANSWER:
False
TYPE: TF DIFFICULTY: Easy
KEYWORDS: descriptive statistics, inferential statistics
12. True or False: Compiling the number of registered voters who turned out to vote for the
primary in Iowa is an example of descriptive statistics.
ANSWER:
True
TYPE: TF DIFFICULTY: Easy
KEYWORDS: descriptive statistics, inferential statistics

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0-4 Getting Started: Important Things to Learn First
13. The Human Resources Director of a large corporation wishes to develop an employee
benefit package and decides to select 500 employees from a list of all (N = 40,000)workers
in order to study their preferences for the various components of a potential package. In
this study, methods involving the collection, presentation, and characterization of the data
are called _______.
ANSWER:
descriptive statistics/methods
TYPE: FI DIFFICULTY: Easy
KEYWORDS: descriptive statistics
14. The Human Resources Director of a large corporation wishes to develop an employee
benefit package and decides to select 500 employees from a list of all (N = 40,000)workers
in order to study their preferences for the various components of a potential package. In
this study, methods that result in decisions concerning population characteristics based only
on the sample results are called _______.
ANSWER:
inferential statistics/methods
TYPE: FI DIFFICULTY: Easy
KEYWORDS: inferential statistics
15. The oranges grown in corporate farms in an agricultural state were damaged by some unknown
fungi a few years ago. Suppose the manager of a large farm wanted to study the impact of the
fungi on the orange crops on a daily basis over a 6-week period. On each day a random sample
of orange trees was selected from within a random sample of acres. The daily average number
of damaged oranges per tree and the proportion of trees having damaged oranges were
calculated. In this study, drawing conclusions on any one day about the true population
characteristics based on information obtained from the sample is called _______.
ANSWER:
inferential statistics/methods
TYPE: FI DIFFICULTY: Moderate
KEYWORDS: inferential statistics
16. The oranges grown in corporate farms in an agricultural state were damaged by some
unknown fungi a few years ago. Suppose the manager of a large farm wanted to study the
impact of the fungi on the orange crops on a daily basis over a 6-week period. On each day
a random sample of orange trees was selected from within a random sample of acres. The
daily average number of damaged oranges per tree and the proportion of trees having
damaged oranges were calculated. In this study, the presentation and characterization of the
two main measures calculated each day (i.e., average number of damaged oranges per tree
and proportion of trees having damaged oranges) is called _______ .
ANSWER:
descriptive statistics/methods
TYPE: FI DIFFICULTY: Moderate
KEYWORDS: descriptive statistics

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Getting Started: Important Things to Learn First 0-5
17. The Commissioner of Health in New York State wanted to study malpractice litigation in
New York. A sample of 31 thousand medical records was drawn from a population of 2.7
million patients who were discharged during 2010. Using the information obtained from
the sample to predict population characteristics with respect to malpractice litigation is an
example of _______.
ANSWER:
inferential statistics
TYPE: FI DIFFICULTY: Moderate
KEYWORDS: inferential statistics
18. The Commissioner of Health in New York State wanted to study malpractice litigation in
New York. A sample of 31 thousand medical records was drawn from a population of 2.7
million patients who were discharged during 2010. The collection, presentation, and
characterization of the data from patient medical records are examples of _______.
ANSWER:
descriptive statistics/methods
TYPE: FI DIFFICULTY: Easy
KEYWORDS: descriptive statistics
19. True or False: Business analytics combine “traditional” statistical methods with methods
and techniques from management science and information systems to form an
interdisciplinary tool that supports fact-based management decision making.
ANSWER:
True
TYPE: TF DIFFICULTY: Easy
KEYWORDS: business analytics
20. Which of the following is not true about business analytics?
a) It enables you to use statistical methods to analyze and explore data to uncover
unforeseen relationships.
b) It enables you to use management science methods to develop optimization models
that impact an organization’s strategy, planning, and operations.
c) It enables you to use complex mathematics to replace the need for organizational
decision making and problem solving.
d) It enables you to use information systems methods to collect and process data sets of
all sizes.
ANSWER:
c
TYPE: MC DIFFICULTY: Moderate
KEYWORDS: business analytics

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0-6 Getting Started: Important Things to Learn First
21. True or False: “Big data” is a concrete concept with a precise operational definition.
ANSWER:
False
TYPE: TF DIFFICULTY: Easy
KEYWORDS: big data
22. True or False: “Big data” are data being collected in huge volumes and at very fast rates,
and they typically arrive in a variety of forms, organized and unorganized.
ANSWER:
True
TYPE: TF DIFFICULTY: Easy
KEYWORDS: big data
23. True or False: In the current data-driven environment of business, the decisions you make
will be increasingly based on gut or intuition supported by personal experience.
ANSWER:
False
TYPE: TF DIFFICULTY: Easy
KEYWORDS: business analytics
24. True or False: The D in the DCOVA framework stands for “data”.
ANSWER:
False
TYPE: TF DIFFICULTY: Easy
KEYWORDS: DCOVA framework
25. True or False: The D in the DCOVA framework stands for “define”.
ANSWER:
True
TYPE: TF DIFFICULTY: Easy
KEYWORDS: DCOVA framework
26. True or False: The C in the DCOVA framework stands for “categorize”.
ANSWER:
False
TYPE: TF DIFFICULTY: Easy
KEYWORDS: DCOVA framework
27. True or False: The C in the DCOVA framework stands for “collect”.
ANSWER:
True
TYPE: TF DIFFICULTY: Easy
KEYWORDS: DCOVA framework

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Getting Started: Important Things to Learn First 0-7
28. True or False: The O in the DCOVA framework stands for “operationalize”.
ANSWER:
False
TYPE: TF DIFFICULTY: Easy
KEYWORDS: DCOVA framework
29. True or False: The O in the DCOVA framework stands for “organize”.
ANSWER:
True
TYPE: TF DIFFICULTY: Easy
KEYWORDS: DCOVA framework
30. True or False: The V in the DCOVA framework stands for “verify”.
ANSWER:
False
TYPE: TF DIFFICULTY: Easy
KEYWORDS: DCOVA framework
31. True or False: The V in the DCOVA framework stands for “visualize”.
ANSWER:
True
TYPE: TF DIFFICULTY: Easy
KEYWORDS: DCOVA framework
32. True or False: The A in the DCOVA framework stands for “apply”.
ANSWER:
False
TYPE: TF DIFFICULTY: Easy
KEYWORDS: DCOVA framework
33. True or False: The V in the DCOVA framework stands for “value”.
ANSWER:
False
TYPE: TF DIFFICULTY: Easy
KEYWORDS: DCOVA framework
34. Which of the following is not an attribute of big data?
a) Variegated
b) Velocity
c) Volume
d) Variety
ANSWER:
a

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0-8 Getting Started: Important Things to Learn First
TYPE: TF DIFFICULTY: Easy
KEYWORDS: business analytics
35. True or False: If rising temperatures are followed immediately by an increase in crime in
urban areas, we would attribute the two events to be an instance of logical causality.
ANSWER:
False
TYPE: TF DIFFICULTY: Moderate
KEYWORDS: Starting points for learning statistics, page 6 text
36. An online marketing system that uses customer demand to adjust its item or service pricing
is using ________ ________ in its strategy.
ANSWER:
dynamic pricing
TYPE: FI DIFFICULTY: Moderate
KEYWORDS: flexibility, demand, page 1 text
37. Using software, you perform calculations more easily than by hand. This means which of
the following:
a) Using software is a guarantee you will not produce inappropriate solutions
b) Memorization of software commands is necessary to using software competently
c) Using the software while understanding the concepts that give the results is more
important than having advanced arithmetic skills.
d) Being able to construct statistical software applications from scratch is a necessary
skill.
ANSWER:
c
TYPE: MC DIFFICULTY: Moderate
KEYWORDS: arithmetic skills, analytical skills
38. The Excel command used to insert a new worksheet into a workbook is
________________ .
ANSWER: INSERT on the worksheet tab
TYPE: FI DIFFICULTY: Moderate
KEYWORDS: EG.4 WORKING WITH a WORKBOOK
39. The Excel command used to display and review formulas is ______________.
ANSWER:
CTRL + ` (grave accent)
TYPE: FI DIFFICULTY: Moderate
KEYWORDS: EG.6 REVIEWING WORKSHEETS

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1-1
Chapter 1: Defining and Collecting Data
1. Which of the following is a discrete quantitative (numerical) variable?
a) The Dow Jones Industrial average
b) The volume of water released from a dam
c) The distance you drove yesterday.
d) The number of employees of an insurance company
ANSWER:
d
TYPE: MC DIFFICULTY: Easy
KEYWORDS: discrete variable, types of data
2. Which of the following is a continuous quantitative (numerical) variable?
a) The color of a student’s eyes
b) The number of employees of an insurance company
c) The amount of milk in a 2-liter carton.
d) The number of gallons of milk sold at the local grocery store yesterday
ANSWER:
c
TYPE: MC DIFFICULTY: Easy
KEYWORDS: continuous variable, types of data
3. To monitor campus security, the campus police office is taking a survey of the number of
students in a parking lot each 30 minutes of a 24-hour period with the goal of determining
when patrols of the lot would serve the most students. If X is the number of students in the
lot each period, then X is an example of
a) a categorical variable.
b) a discrete variable.
c) a continuous variable.
d) a statistic.
ANSWER:
b
TYPE: MC DIFFICULTY: Moderate
KEYWORDS: discrete variable, types of data
4. Researchers are concerned that the weight of the average American school child is
increasing implying, among other things, that children’s clothing should be manufactured
and marketed in larger sizes. If X is the weight of school children sampled in a nationwide
study without rounding, then X is an example of
a) a categorical variable.
b) a discrete variable.
c) a continuous variable.
d) a table of random numbers.

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1-2 Chapter 1: Defining and Collecting Data
ANSWER:
c
TYPE: MC DIFFICULTY: Moderate
KEYWORDS: continuous variable, types of data
5. The classification of student class designation (freshman, sophomore, junior, senior) is an
example of
a) a categorical variable.
b) a discrete variable.
c) a continuous variable.
d) a table of random numbers.
ANSWER:
a
TYPE: MC DIFFICULTY: Easy
KEYWORDS: categorical variable, types of data
6. The classification of student major (accounting, economics, management, marketing, other)
is an example of
a) a categorical variable.
b) a discrete variable.
c) a continuous variable.
d) a table of random numbers.
ANSWER:
a
TYPE: MC DIFFICULTY: Easy
KEYWORDS: categorical variable, types of data
7. The chancellor of a major university was concerned about alcohol abuse on her campus and
wanted to find out the proportion of students at her university who visited campus bars on
the weekend before the final exam week. Her assistant took a random sample of 250
students. The answer on “whether you visited campus bars on the weekend before the final
exam week” from students in the sample is an example of __________.
a) a categorical variable.
b) a discrete variable.
c) a continuous variable.
d) a table of random numbers.
ANSWER:
a
TYPE: MC DIFFICULTY: Easy
KEYWORDS: categorical variable, types of data

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Chapter 1: Defining and Collecting Data 1-3
SCENARIO 1-1
The manager of the customer service division of a major consumer electronics company is
interested in determining whether the customers who have purchased a Blu-ray player made by
the company over the past 12 months are satisfied with their products.
8. Referring to Scenario 1-1, the possible responses to the question "How many Blu-ray
players made by other manufacturers have you used?" are values from a
a) discrete variable.
b) continuous variable.
c) categorical variable.
d) table of random numbers.
ANSWER:
a
TYPE: MC DIFFICULTY: Easy
KEYWORDS: discrete variable, types of data
9. Referring to Scenario 1-1, the possible responses to the question "Are you happy,
indifferent, or unhappy with the performance per dollar spent on the Blu-ray player?" are
values from a
a) discrete numerical variable.
b) continuous numerical variable.
c) categorical variable.
d) table of random numbers.
ANSWER:
c
TYPE: MC DIFFICULTY: Easy
KEYWORDS: categorical variable, types of data
10. Referring to Scenario 1-1, the possible responses to the question "What is your annual
income rounded to the nearest thousands?" are values from a
a) discrete numerical variable.
b) continuous numerical variable.
c) categorical variable.
d) table of random numbers.
ANSWER:
a
TYPE: MC DIFFICULTY: Difficult
KEYWORDS: discrete variable, types of data
EXPLANATION: Even though money is usually considered as a continuous variable, it should
be considered as a discrete variable when rounded to the nearest thousands.

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1-4 Chapter 1: Defining and Collecting Data
11. Referring to Scenario 1-1, the possible responses to the question "How much time do you
use the Blu-ray player every week on the average?" are values from a
a) discrete numerical variable.
b) continuous numerical variable.
c) categorical variable.
d) table of random numbers.
ANSWER:
b
TYPE: MC DIFFICULTY: Easy
KEYWORDS: continuous variable, types of data
12. Referring to Scenario 1-1, the possible responses to the question "How many people are
there in your household?" are values from a
a) discrete numerical variable.
b) continuous numerical variable.
c) categorical variable.
d) table of random numbers.
ANSWER:
a
TYPE: MC DIFFICULTY: Easy
KEYWORDS: discrete variable, types of data
13. Referring to Scenario 1-1, the possible responses to the question "How would you rate the
quality of your purchase experience with 1 = excellent, 2 = good, 3 = decent, 4 = poor, 5 =
terrible?" are values from a
a) discrete numerical variable.
b) continuous numerical variable.
c) categorical variable.
d) table of random numbers.
ANSWER:
c
TYPE: MC DIFFICULTY: Easy
KEYWORDS: categorical variable, types of data
14. Referring to Scenario 1-1, the possible responses to the question "What brand of Blu-ray
player did you purchase?" are values from a
a) discrete numerical variable.
b) continuous numerical variable.
c) categorical variable.
d) table of random numbers.
ANSWER:
c
TYPE: MC DIFFICULTY: Easy
KEYWORDS: categorical variable, types of data

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