Business Statistics
ST
11th Edition
UV
TEST BANK
IA
?_
AP
Thomas A. Black
PR
Comprehensive Test Bank for Instructors
OV
and Students
ED
© Thomas A. Black. All rights reserved. Reproduction or distribution without permission is
prohibited.
??
©DREAMSHUB
, Business Statistics: For Contemporary Decision Making – 11th Edition
(ISBN 9781119905448 – Verified)
Ken Black
ST
TABLE OF CONTENTS
Chapter 1. Introduction to Statistics and Business Analytics
UV
Chapter 2. Visualizing Data with Charts and Graphs
Chapter 3. Descriptive Statistics
Chapter 4. Probability
Chapter 5. Discrete Distributions
Chapter 6. Continuous Distributions
IA
Chapter 7. Sampling and Sampling Distributions
Chapter 8. Statistical Inference: Estimation for Single Populations
Chapter 9. Statistical Inference: Hypothesis Testing for Single Populations
?_
Chapter 10. Statistical Inferences About Two Populations
Chapter 11. Analysis of Variance and Design of Experiments
Chapter 12. Simple Regression Analysis and Correlation
Chapter 13. Multiple Regression Analysis
AP
Chapter 14. Building Multiple Regression Models
Chapter 15. Time-Series Forecasting and Index Numbers
Chapter 16. Analysis of Categorical Data
Chapter 17. Nonparametric Statistics
PR
Chapter 18. Statistical Quality Control
Chapter 19. Decision Analysis
OV
ED
??
©DREAMSHUB
, CHAPTER 1
INTRODUCTION TO STATISTICS AND BUSINESS
ST
ANALYTICS
CHAPTER LEARNING OBJECTIVES
UV
1. Define important statistical terms, including population, sample, and parameter, as
they relate to descriptive and inferential statistics. The study of statistics can be subdivided
into two main areas: descriptive statistics and inferential statistics. Descriptive statistics result
from gathering data from a body, group, or population and reaching conclusions only about that
group. Inferential statistics are generated from the process of gathering sample data from a
IA
group, body, or population and reaching conclusions about the larger group from which the
sample was drawn.
?_
2. Explain the difference between variables, measurement, and data, and compare the
four different levels of data: nominal, ordinal, interval, and ratio. Most business statistics
studies contain variables, measurements, and data. A variable is a characteristic of any entity
being studied that is capable of taking on different values. Examples of variables might include
monthly household food spending, time between arrivals at a restaurant, and patient satisfaction
AP
rating. A measurement is when a standard process is used to assign numbers to particular
attributes or characteristics of a variable. Measurements of monthly household food spending
might be taken in dollars, time between arrivals might be measured in minutes, and patient
satisfaction might be measured using a 5-point scale. Data are recorded measurements. It is
data that are analyzed by business statisticians in order to learn more about the variables being
studied. Two major types of inferential statistics are (1) parametric statistics and (2)
PR
nonparametric statistics. Use of parametric statistics requires interval or ratio data and certain
assumptions about the distribution of the data. The techniques presented in this text are largely
parametric. If data are only nominal or ordinal in level, nonparametric statistics must be used.
The appropriate type of statistical analysis depends on the level of data measurement, which
can be (1) nominal, (2) ordinal, (3) interval, or (4) ratio. Nominal is the lowest level, representing
OV
the classification of only data such as geographic location, sex, or social insurance number. The
next level is ordinal, which provides rank ordering measurements in which the intervals between
consecutive numbers do not necessarily represent equal distances. Interval is the next to
highest level of data measurement, in which the distances represented by consecutive numbers
are equal. The highest level of data measurement is ratio, which has all the qualities of interval
measurement, but ratio data contain an absolute zero and ratios between numbers are
meaningful. Interval and ratio data are sometimes called metric or quantitative data. Nominal
ED
and ordinal data are sometimes called nonmetric or qualitative data.
3. Explain the differences between the four dimensions of big data. The data that is
available to decision makers is exponentially growing, as are the sources for that data. This
??
growth has resulted in a new set of data called ‘big data’. Big data is defined as a collection of
large and complex datasets from different sources that are difficult to process using traditional
data management and processing applications. There are four key characteristics associated
Copyright © 2023 John Wiley & Sons Canada, Ltd. Unauthorized copying, distribution, or transmission of this page is prohibited
, Introduction to Statistics 1-2
with big data and they are: variety, velocity, veracity and volume. Each of these characteristics
are discussed in the text.
The computer allows for the storage, retrieval, and transfer of large data sets. Furthermore,
computer soft ware has been developed to analyze data by means of sophisticated statistical
ST
techniques. Business statisticians use many popular statistical soft ware packages, including
Minitab, SAS, and SPSS. In this text, the computer statistical output presented is from the
Microsoft Excel software, which in spite of its limitations, is the most commonly used package in
the business environment.
UV
4. Compare and contrast the three categories of business analytics. There are three main
categories of business analytics, or the application of processes and techniques that transform
raw data into meaningful information to improve decision making. The three categories are
descriptive analytics, predictive analytics and prescriptive analytics. Descriptive analytics
describe what has or is happening relative to the data collected. On the other hand, predictive
analytics which look to find relationships in the data. Tools in this category include regression
IA
analysis, time-series and forecasting; all of which are designed to allow management to
estimate what might happen based on a given set of criteria or circumstance. The last category
is prescriptive analytics which take risk into account when analyzing data and making decisions
based on that data. Examples of where prescriptive analytics may be used include performance
?_
management or network analysis.
5. Describe the data mining and data visualization processes. Data mining is the process
of collecting, exploring and analyzing large volumes of data in an effort to uncover hidden
AP
patterns and/or relationships that can be used to enhance business decision-making. Data
mining allows businesses to take large amounts of data, pull out what they need to facilitate
decision making. Data visualization is the study of visual representation of data and is
employed to convey data or information by imparting it as visual objects displayed in graphics.
By presenting the information or data visually can make the data and data results more
understandable and thereby more useable.
PR
OV
ED
??
Copyright © 2023 John Wiley & Sons Canada, Ltd. Unauthorized copying, distribution, or transmission of this page is prohibited
ST
11th Edition
UV
TEST BANK
IA
?_
AP
Thomas A. Black
PR
Comprehensive Test Bank for Instructors
OV
and Students
ED
© Thomas A. Black. All rights reserved. Reproduction or distribution without permission is
prohibited.
??
©DREAMSHUB
, Business Statistics: For Contemporary Decision Making – 11th Edition
(ISBN 9781119905448 – Verified)
Ken Black
ST
TABLE OF CONTENTS
Chapter 1. Introduction to Statistics and Business Analytics
UV
Chapter 2. Visualizing Data with Charts and Graphs
Chapter 3. Descriptive Statistics
Chapter 4. Probability
Chapter 5. Discrete Distributions
Chapter 6. Continuous Distributions
IA
Chapter 7. Sampling and Sampling Distributions
Chapter 8. Statistical Inference: Estimation for Single Populations
Chapter 9. Statistical Inference: Hypothesis Testing for Single Populations
?_
Chapter 10. Statistical Inferences About Two Populations
Chapter 11. Analysis of Variance and Design of Experiments
Chapter 12. Simple Regression Analysis and Correlation
Chapter 13. Multiple Regression Analysis
AP
Chapter 14. Building Multiple Regression Models
Chapter 15. Time-Series Forecasting and Index Numbers
Chapter 16. Analysis of Categorical Data
Chapter 17. Nonparametric Statistics
PR
Chapter 18. Statistical Quality Control
Chapter 19. Decision Analysis
OV
ED
??
©DREAMSHUB
, CHAPTER 1
INTRODUCTION TO STATISTICS AND BUSINESS
ST
ANALYTICS
CHAPTER LEARNING OBJECTIVES
UV
1. Define important statistical terms, including population, sample, and parameter, as
they relate to descriptive and inferential statistics. The study of statistics can be subdivided
into two main areas: descriptive statistics and inferential statistics. Descriptive statistics result
from gathering data from a body, group, or population and reaching conclusions only about that
group. Inferential statistics are generated from the process of gathering sample data from a
IA
group, body, or population and reaching conclusions about the larger group from which the
sample was drawn.
?_
2. Explain the difference between variables, measurement, and data, and compare the
four different levels of data: nominal, ordinal, interval, and ratio. Most business statistics
studies contain variables, measurements, and data. A variable is a characteristic of any entity
being studied that is capable of taking on different values. Examples of variables might include
monthly household food spending, time between arrivals at a restaurant, and patient satisfaction
AP
rating. A measurement is when a standard process is used to assign numbers to particular
attributes or characteristics of a variable. Measurements of monthly household food spending
might be taken in dollars, time between arrivals might be measured in minutes, and patient
satisfaction might be measured using a 5-point scale. Data are recorded measurements. It is
data that are analyzed by business statisticians in order to learn more about the variables being
studied. Two major types of inferential statistics are (1) parametric statistics and (2)
PR
nonparametric statistics. Use of parametric statistics requires interval or ratio data and certain
assumptions about the distribution of the data. The techniques presented in this text are largely
parametric. If data are only nominal or ordinal in level, nonparametric statistics must be used.
The appropriate type of statistical analysis depends on the level of data measurement, which
can be (1) nominal, (2) ordinal, (3) interval, or (4) ratio. Nominal is the lowest level, representing
OV
the classification of only data such as geographic location, sex, or social insurance number. The
next level is ordinal, which provides rank ordering measurements in which the intervals between
consecutive numbers do not necessarily represent equal distances. Interval is the next to
highest level of data measurement, in which the distances represented by consecutive numbers
are equal. The highest level of data measurement is ratio, which has all the qualities of interval
measurement, but ratio data contain an absolute zero and ratios between numbers are
meaningful. Interval and ratio data are sometimes called metric or quantitative data. Nominal
ED
and ordinal data are sometimes called nonmetric or qualitative data.
3. Explain the differences between the four dimensions of big data. The data that is
available to decision makers is exponentially growing, as are the sources for that data. This
??
growth has resulted in a new set of data called ‘big data’. Big data is defined as a collection of
large and complex datasets from different sources that are difficult to process using traditional
data management and processing applications. There are four key characteristics associated
Copyright © 2023 John Wiley & Sons Canada, Ltd. Unauthorized copying, distribution, or transmission of this page is prohibited
, Introduction to Statistics 1-2
with big data and they are: variety, velocity, veracity and volume. Each of these characteristics
are discussed in the text.
The computer allows for the storage, retrieval, and transfer of large data sets. Furthermore,
computer soft ware has been developed to analyze data by means of sophisticated statistical
ST
techniques. Business statisticians use many popular statistical soft ware packages, including
Minitab, SAS, and SPSS. In this text, the computer statistical output presented is from the
Microsoft Excel software, which in spite of its limitations, is the most commonly used package in
the business environment.
UV
4. Compare and contrast the three categories of business analytics. There are three main
categories of business analytics, or the application of processes and techniques that transform
raw data into meaningful information to improve decision making. The three categories are
descriptive analytics, predictive analytics and prescriptive analytics. Descriptive analytics
describe what has or is happening relative to the data collected. On the other hand, predictive
analytics which look to find relationships in the data. Tools in this category include regression
IA
analysis, time-series and forecasting; all of which are designed to allow management to
estimate what might happen based on a given set of criteria or circumstance. The last category
is prescriptive analytics which take risk into account when analyzing data and making decisions
based on that data. Examples of where prescriptive analytics may be used include performance
?_
management or network analysis.
5. Describe the data mining and data visualization processes. Data mining is the process
of collecting, exploring and analyzing large volumes of data in an effort to uncover hidden
AP
patterns and/or relationships that can be used to enhance business decision-making. Data
mining allows businesses to take large amounts of data, pull out what they need to facilitate
decision making. Data visualization is the study of visual representation of data and is
employed to convey data or information by imparting it as visual objects displayed in graphics.
By presenting the information or data visually can make the data and data results more
understandable and thereby more useable.
PR
OV
ED
??
Copyright © 2023 John Wiley & Sons Canada, Ltd. Unauthorized copying, distribution, or transmission of this page is prohibited
