,TESTBANK For Business Statistics 10e Black
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,Black / Business Statistics, 10th Edition Test Bank
File: ch01, Chapter 1: Introduction to Statistics
True/False
1. Virtually all areas of business use statistics in decision making.
Ans: True
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
2. Statistics can be used to predict business in the future.
Ans: True
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
3. Statistics are used to market vitamins.
Ans: True
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
4. A list of final grades in an introductory class in business is an example of statistics
Ans: False
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
5. A graph of purchases made from one store location would be an example of statistics within a business
context.
Ans: True
Response: See section 1.1: Basic Statistical Concepts
Difficulty: Easy
Leaning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
6. The complete collection of all entities under study is called the sample.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
6. A portion or subset of the entities under study is called the statistic.
Copyright ©2019 John Wiley & Sons, Inc. 1-1
,Black / Business Statistics, 10th Edition Test Bank
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
8. A descriptive measure of the population is called a parameter.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
9. A census is the process of gathering data on all the entities in the population.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
10. Statistics is commonly divided into two branches called descriptive statistics and summary statistics.
Ans: False
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
11. A descriptive measure of the sample is called a statistic.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
12. Gathering data from a sample to reach conclusions about the population from which the sample was
drawn is called descriptive statistics.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Medium
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
Copyright ©2019 John Wiley & Sons, Inc. 1-2
,Black / Business Statistics, 10th Edition Test Bank
13. Calculation of population parameters is usually either impossible or excessively time consuming and
costly.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
14. The basis for inferential statistics is the ability to make decisions about population parameters without
having to complete a census of the population.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
15. A variable is a numerical description of each of the possible outcomes of an experiment.
Ans: True
Response: See section 1.3 Introduction to Business Analytics
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
16. Variables and measurement data are interchangeable terms.
Ans: False
Response: See section 1.3 Introduction to Business Analytics
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
17. Measurements occur when a standard process is used to assign numbers to attributes or characteristics
of a variable.
Ans: True
Response: See section 1.3 Introduction to Business Analytics
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
18. One piece of data includes a variety of variables.
Ans: False
Response: See section 1.3 Introduction to Business Analytics
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
19. A variable can take on different values.
Ans: True
Copyright ©2019 John Wiley & Sons, Inc. 1-3
,Black / Business Statistics, 10th Edition Test Bank
Response: See section 1.1 Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.3 : Explain the difference between variables, measurement, and data.
20. All numerical data must be analyzed statistically in the same way because all of them are represented
by numbers.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
21. The manner in which numerical data can be analyzed statistically depends on the level of data
measurement represented by numbers being analyzed.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
22. The lowest level of data measurement is the ratio level.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
23. The highest level of data measurement is the ratio level.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
24. Numbers which are used only to classify or categorize the observations represent data measured at the
nominal level.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
25. Numbers which are used to rank-order the performance of workers represent data measured at the
interval level.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Medium
Copyright ©2019 John Wiley & Sons, Inc. 1-4
,Black / Business Statistics, 10th Edition Test Bank
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
26. Nominal and ordinal data are sometimes referred to as qualitative data.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
27. Nominal and ordinal data are sometimes referred to as quantitative data.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
28. With interval data, the zero point is a matter of convention and does not mean the absence of the
phenomenon under observation.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
29. Interval and Ratio data are sometimes referred to as quantitative data.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
30. Big data refers to a standard set of variables collected from customers, suppliers, and staff.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.5: Define important business analytics terms including big data, business analytics,
data mining, and data visualization.
31. One goal of data visualization is to make complex data easier to understand.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.5: Define important business analytics terms including big data, business analytics,
data mining, and data visualization.
Copyright ©2019 John Wiley & Sons, Inc. 1-5
,Black / Business Statistics, 10th Edition Test Bank
32. The main objective of business analytics is to transform data into meaningful information for
business managers.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.5: Define important business analytics terms including big data, business analytics,
data mining, and data visualization.
33. Extracting and transforming data are two steps in data visualization.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.5: Define important business analytics terms including big data, business analytics,
data mining, and data visualization.
34. If a manager relies on his/her gut instinct to make critical business decisions, this is an example of
business analytics in action.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.5: Define important business analytics terms including big data, business analytics,
data mining, and data visualization.
35. If big data has variety, then it can be said that the data are from several different sources such as
videos, retail scanners, and the internet.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.
36. Velocity refers to the speed with which data are available to the business for analysis.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.
37. The term “garbage in, garbage out” refers to the volume of the data used by a business.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Copyright ©2019 John Wiley & Sons, Inc. 1-6
,Black / Business Statistics, 10th Edition Test Bank
Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.
38. Big data can include unstructured data such as writings and photographs.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.
39. Big data should encompass all four characteristics of variety, velocity, virtuous, and volume.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.
40. Descriptive statistics focuses on what has happened or is happening within the business.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.7: Compare and contrast the three categories of business analytics.
41. Prescriptive analytics is the second step in big data analysis, following descriptive statistics.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.7: Compare and contrast the three categories of business analytics.
42. Prescriptive analytics is optimal for taking risk and uncertainty into account by looking at the effects
of future actions.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.7: Compare and contrast the three categories of business analytics.
43. Simulation is a mathematical strategy one would expect to find within both predictive and prescriptive
analytics.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.7: Compare and contrast the three categories of business analytics.
Copyright ©2019 John Wiley & Sons, Inc. 1-7
, Black / Business Statistics, 10th Edition Test Bank
44. The three categories of business analytics could be described as describing what has happened,
predicting potential relationships among data, and prescribing future decisions under uncertainty.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.7: Compare and contrast the three categories of business analytics.
Multiple Choice
45. Which of the following statements about business statistics is not true?
a) Virtually every area of business uses statistics in decision making.
b) Presenting business statistics always requires the use of a specific graph called a bar chart.
c) There is a wide variety of uses and applications of statistics in business.
d) Business statistics can be used to forecast future values and predict trends.
Ans: b
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
46. A book publisher uses statistics in decision-making. Of the following statistics, which would this
publisher not consider in their decisions?
a) Trends in purchases of hard copy and ebooks.
b) The cost of paper.
c) Trends in attendance at book clubs
d) Trends in local grocery stores
e) Revenue of competitors
Ans: d
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Medium
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
47. Which of the following would be the least helpful type of data to a car manufacturer when making
business decisions?
a) Economic data
b) School attendance data
c) Financial data
d) Competitor data
e) Employment data
Copyright ©2019 John Wiley & Sons, Inc. 1-8
Hello all ,
We have all what you need with best price
Our email :
Our website :
testbanks-store.com
,Black / Business Statistics, 10th Edition Test Bank
File: ch01, Chapter 1: Introduction to Statistics
True/False
1. Virtually all areas of business use statistics in decision making.
Ans: True
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
2. Statistics can be used to predict business in the future.
Ans: True
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
3. Statistics are used to market vitamins.
Ans: True
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
4. A list of final grades in an introductory class in business is an example of statistics
Ans: False
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
5. A graph of purchases made from one store location would be an example of statistics within a business
context.
Ans: True
Response: See section 1.1: Basic Statistical Concepts
Difficulty: Easy
Leaning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
6. The complete collection of all entities under study is called the sample.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
6. A portion or subset of the entities under study is called the statistic.
Copyright ©2019 John Wiley & Sons, Inc. 1-1
,Black / Business Statistics, 10th Edition Test Bank
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
8. A descriptive measure of the population is called a parameter.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
9. A census is the process of gathering data on all the entities in the population.
Ans: True
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
10. Statistics is commonly divided into two branches called descriptive statistics and summary statistics.
Ans: False
Response: See section 1.2, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
11. A descriptive measure of the sample is called a statistic.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
12. Gathering data from a sample to reach conclusions about the population from which the sample was
drawn is called descriptive statistics.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Medium
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
Copyright ©2019 John Wiley & Sons, Inc. 1-2
,Black / Business Statistics, 10th Edition Test Bank
13. Calculation of population parameters is usually either impossible or excessively time consuming and
costly.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
14. The basis for inferential statistics is the ability to make decisions about population parameters without
having to complete a census of the population.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.2: Define important statistical terms, including population, sample, and parameter,
as they relate to descriptive and inferential statistics.
15. A variable is a numerical description of each of the possible outcomes of an experiment.
Ans: True
Response: See section 1.3 Introduction to Business Analytics
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
16. Variables and measurement data are interchangeable terms.
Ans: False
Response: See section 1.3 Introduction to Business Analytics
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
17. Measurements occur when a standard process is used to assign numbers to attributes or characteristics
of a variable.
Ans: True
Response: See section 1.3 Introduction to Business Analytics
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
18. One piece of data includes a variety of variables.
Ans: False
Response: See section 1.3 Introduction to Business Analytics
Difficulty: Medium
Learning Objective: 1.3: Explain the difference between variables, measurement, and data.
19. A variable can take on different values.
Ans: True
Copyright ©2019 John Wiley & Sons, Inc. 1-3
,Black / Business Statistics, 10th Edition Test Bank
Response: See section 1.1 Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.3 : Explain the difference between variables, measurement, and data.
20. All numerical data must be analyzed statistically in the same way because all of them are represented
by numbers.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
21. The manner in which numerical data can be analyzed statistically depends on the level of data
measurement represented by numbers being analyzed.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
22. The lowest level of data measurement is the ratio level.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
23. The highest level of data measurement is the ratio level.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
24. Numbers which are used only to classify or categorize the observations represent data measured at the
nominal level.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
25. Numbers which are used to rank-order the performance of workers represent data measured at the
interval level.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Medium
Copyright ©2019 John Wiley & Sons, Inc. 1-4
,Black / Business Statistics, 10th Edition Test Bank
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
26. Nominal and ordinal data are sometimes referred to as qualitative data.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
27. Nominal and ordinal data are sometimes referred to as quantitative data.
Ans: False
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
28. With interval data, the zero point is a matter of convention and does not mean the absence of the
phenomenon under observation.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Medium
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
29. Interval and Ratio data are sometimes referred to as quantitative data.
Ans: True
Response: See section 1.2, Data Measurement
Difficulty: Easy
Learning Objective: 1.4: Compare the four different levels of data: nominal, ordinal, interval, and ratio.
30. Big data refers to a standard set of variables collected from customers, suppliers, and staff.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.5: Define important business analytics terms including big data, business analytics,
data mining, and data visualization.
31. One goal of data visualization is to make complex data easier to understand.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.5: Define important business analytics terms including big data, business analytics,
data mining, and data visualization.
Copyright ©2019 John Wiley & Sons, Inc. 1-5
,Black / Business Statistics, 10th Edition Test Bank
32. The main objective of business analytics is to transform data into meaningful information for
business managers.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.5: Define important business analytics terms including big data, business analytics,
data mining, and data visualization.
33. Extracting and transforming data are two steps in data visualization.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.5: Define important business analytics terms including big data, business analytics,
data mining, and data visualization.
34. If a manager relies on his/her gut instinct to make critical business decisions, this is an example of
business analytics in action.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.5: Define important business analytics terms including big data, business analytics,
data mining, and data visualization.
35. If big data has variety, then it can be said that the data are from several different sources such as
videos, retail scanners, and the internet.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.
36. Velocity refers to the speed with which data are available to the business for analysis.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.
37. The term “garbage in, garbage out” refers to the volume of the data used by a business.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Copyright ©2019 John Wiley & Sons, Inc. 1-6
,Black / Business Statistics, 10th Edition Test Bank
Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.
38. Big data can include unstructured data such as writings and photographs.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.
39. Big data should encompass all four characteristics of variety, velocity, virtuous, and volume.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.6: List the four dimensions of big data and explain the differences between them.
40. Descriptive statistics focuses on what has happened or is happening within the business.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.7: Compare and contrast the three categories of business analytics.
41. Prescriptive analytics is the second step in big data analysis, following descriptive statistics.
Ans: False
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.7: Compare and contrast the three categories of business analytics.
42. Prescriptive analytics is optimal for taking risk and uncertainty into account by looking at the effects
of future actions.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.7: Compare and contrast the three categories of business analytics.
43. Simulation is a mathematical strategy one would expect to find within both predictive and prescriptive
analytics.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.7: Compare and contrast the three categories of business analytics.
Copyright ©2019 John Wiley & Sons, Inc. 1-7
, Black / Business Statistics, 10th Edition Test Bank
44. The three categories of business analytics could be described as describing what has happened,
predicting potential relationships among data, and prescribing future decisions under uncertainty.
Ans: True
Response: See section 1.3: Introduction to Business Analytics
Difficulty: Easy
Learning Objective: 1.7: Compare and contrast the three categories of business analytics.
Multiple Choice
45. Which of the following statements about business statistics is not true?
a) Virtually every area of business uses statistics in decision making.
b) Presenting business statistics always requires the use of a specific graph called a bar chart.
c) There is a wide variety of uses and applications of statistics in business.
d) Business statistics can be used to forecast future values and predict trends.
Ans: b
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Easy
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
46. A book publisher uses statistics in decision-making. Of the following statistics, which would this
publisher not consider in their decisions?
a) Trends in purchases of hard copy and ebooks.
b) The cost of paper.
c) Trends in attendance at book clubs
d) Trends in local grocery stores
e) Revenue of competitors
Ans: d
Response: See section 1.1, Basic Statistical Concepts
Difficulty: Medium
Learning Objective: 1.1: List quantitative and graphical examples of statistics within a business context.
47. Which of the following would be the least helpful type of data to a car manufacturer when making
business decisions?
a) Economic data
b) School attendance data
c) Financial data
d) Competitor data
e) Employment data
Copyright ©2019 John Wiley & Sons, Inc. 1-8
