Statistics For Managers Using Microsoft Excel, Global 8th Edition Solution Manual

Name: Statistics For Managers Using Microsoft Excel, Global 8th Edition Solution Manual
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Author: Benjamin Fisher

Statistics For Managers Using Microsoft Excel, Global 8th Edition Solution Manual delivers clear and concise summaries of essential textbook content.

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Table of Contents
Teaching Tips...................................................................................................................................1
Chapter 1 Defining and Collecting Data............................................................................................... 39
Chapter 2 Organizing and Visualizing Variables ................................................................................. 46
Chapter 3 Numerical Descriptive Measures ..................................................................................... ..141
Chapter 4 Basic Probability ................................................................................................................ 183
Chapter 5 Discrete Probability Distributions ...................................................................................... 190
Chapter 6 The Normal Distribution and Other Continuous Distributions .......................................... 215
Chapter 7 Sampling Distributions....................................................................................................... 241
Chapter 8 Confidence Interval Estimation.......................................................................................... 261
Chapter 9 Fundamentals of Hypothesis Testing: One-Sample Tests.................................................. 292
Chapter 10 Two-Sample Tests............................................................................................................. 337
Chapter 11 Analysis of Variance .......................................................................................................... 415
Chapter 12 Chi-Square and Nonparametric Tests ................................................................................ 441
Chapter 13 Simple Linear Regression .................................................................................................. 482
Chapter 14 Introduction to Multiple Regression .................................................................................. 521
Chapter 15 Multiple Regression Model Building ................................................................................. 584
Chapter 16 Time-Series Forecasting..................................................................................................... 641
Chapter 17 Getting Ready to Analyze Data in the Future .................................................................... 696
Chapter 18 Statistical Applications in Quality Management (Online) ................................................. 748
Chapter 19 Decision Making (Online).................................................................................................. 781
Online Sections .................................................................................................................. 820
Instructional Tips and Solutions for Digital Cases............................................................. 883
The Brynne Packaging Case .............................................................................................. 918
The CardioGood Fitness Case ........................................................................................... 920

The Choice Is Yours/More Descriptive Choices Follow-up Case .................................... 1043
The Clear Mountain State Student Surveys Case ............................................................. 1130
The Craybill Instrumentation Company Case.................................................................. 1230
The Managing Ashland MultiComm Services Case ......................................................... 1232
The Mountain States Potato Company Case .................................................................... 1287
The Sure Value Convenience Stores Case........................................................................ 1296

Preface
The first part of the Instructor’s Solutions Manual contains our educational philosophy and teaching tips
for each chapter of the text. Solutions to End-of-Section Problems and Chapter Review Problems in each
chapter follow. Instructional tips and solutions for the digital cases are included next. Answers to the
Brynne Packaging Case, the CardioGood Fitness Case, the Choice Is Yours/More Descriptive Choices
Follow-up Case, the Clear Mountain State Student Surveys Case, the Craybill Instrumentation Company
Case, the Managing Ashland MultiComm Services Case, the Mountain States Potato Company Case and
the Sure Value Convenience Stores Case are included last.
The purpose of this Instructor’s Solutions Manual is to facilitate grading of assignments or exams by
instructors and/or teaching assistants. Screen shots using output from PHStat are integrated throughout.
Most of the problems are solved using PHStat. To present the steps involved in solving a problem, some
intermediate numerical results are presented accurate to only a reasonable number of significant digits.
Hence, instructors are reminded that the final results presented in this manual that are obtained using
PHStat can sometimes be different from those obtained with a hand calculator computed using the
intermediate values due to rounding.

Teaching Tips for
Statistics for Managers using Microsoft® Excel 8th Edition,
Global Edition
Our Starting Point
Over a generation ago, advances in “data processing” led to new business opportunities as first
centralized and then desktop computing proliferated. The Information Age was born. Computer
science became much more than just an adjunct to a mathematics curriculum, and whole new
fields of studies, such as computer information systems, emerged.
More recently, further advances in information technologies have combined with data
analysis techniques to create new opportunities in what is more data science than data processing
or computer science. The world of business statistics has grown larger, bumping into other
disciplines. And, in a reprise of something that occurred a generation ago, new fields of study,
this time with names such as informatics, data analytics, and decision science, have emerged.
This time of change makes what is taught in business statistics and how it is taught all the
more critical. These new fields of study all share statistics as a foundation for further learning.
We are accustomed to thinking about change, as seeking ways to continuously improve the
teaching of business statistics have always guided our efforts. We actively participate in Decision
Sciences Institute (DSI), American Statistical Association (ASA), and Making Statistics More
Effective in Schools and Business (MSMESB) conferences. We use the ASA’s Guidelines for
Assessment and Instruction (GAISE) reports and combine them with our experiences teaching
business statistics to a diverse student body at several large universities.
What to teach and how to teach it are particularly significant questions to ask during a time of
change. As an author team, we bring a unique collection of experiences that we believe helps us
find the proper perspective in balancing the old and the new. Our lead author, David M. Levine,
was the first educator, along with Mark L. Berenson, to create a business statistics textbook that

discussed using statistical software and incorporated “computer output” as illustrations—just the
first of many teaching and curricular innovations in his many years of teaching business statistics.
Our second author, David F. Stephan, developed courses and teaching methods in computer
information systems and digital media during the information revolution, creating, and then
teaching in, one of the first personal computer classrooms in a large school of business along the
way. Early in his career, he introduced spreadsheet applications to a business statistics faculty
audience that included David Levine, an introduction that would eventually led to the first edition
of this textbook. Our newest co-author, Kathryn A. Szabat, has provided statistical advice to
various business and non-business communities. Her background in statistics and operations
research and her experiences interacting with professionals in practice have guided her, as
departmental chair, in developing a new, interdisciplinary academic department, Business
Systems and Analytics, in response to the technology- and data-driven changes in business today.
All three of us benefit from our many years teaching undergraduate business subjects and the
diversity of interests and efforts of our past co-authors, Mark Berenson and Timothy Krehbiel.
Two of us (Stephan and Szabat) also benefit from formal training and background in educational
methods and instructional design.
Educational Philosophy
As in prior editions of Statistics for Managers Using Microsoft Excel, we are guided by these
key learning principles:
1. Help students see the relevance of statistics to their own careers by providing examples
drawn from the functional areas in which they may be specializing. Students need a
frame of reference when learning statistics, especially when statistics is not their major. That
frame of reference for business students should be the functional areas of business, such as
accounting, finance, information systems, management, and marketing. Each statistics topic

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needs to be presented in an applied context related to at least one of these functional areas.
The focus in teaching each topic should be on its application in business, the interpretation of
results, the evaluation of the assumptions, and the discussion of what should be done if the
assumptions are violated.
2. Emphasize interpretation of statistical results over mathematical computation.
Introductory business statistics courses should recognize the growing need to interpret
statistical results that computerized processes create. This makes the interpretation of results
more important than knowing how to execute the tedious hand calculations required to
produce them.
3. Give students ample practice in understanding how to apply statistics to business. Both
classroom examples and homework exercises should involve actual or realistic data as much
as possible. Students should work with data sets, both small and large, and be encouraged to
look beyond the statistical analysis of data to the interpretation of results in a managerial
context.
4. Familiarize students with how to use statistical software to assist business decision-
making. Introductory business statistics courses should recognize that programs with
statistical functions are commonly found on a business decision maker’s desktop computer.
Integrating statistical software into all aspects of an introductory statistics course allows the
course to focus on interpretation of results instead of computations (see point 2).
5. Provide clear instructions to students for using statistical applications. Books should
explain clearly how to use programs such as Microsoft Excel with the study of statistics,
without having those instructions dominate the book or distract from the learning of statistical
concepts.

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First Things First
In a time of change, you can never know exactly what knowledge and background students bring
into an introductory business statistics classroom. Add that to the need to curb the fear factor
about learning statistics that so many students begin with, and there’s a lot to cover even before
you teach your first statistical concept.
We created “First Things First” to meet this challenge. This unit sets the context for
explaining what statistics is (not what students may think!) while ensuring that all students share
an understanding of the forces that make learning business statistics critically important today.
Especially designed for instructors teaching with course management tools, including those
teaching hybrid or online courses, “First Things First” has been developed to be posted online or
otherwise distributed before the first class section begins and is available from the download page
for this book that is discussed in Appendix Section C.1.
We would argue that the most important class is the first class. First impressions are critically
important. You have the opportunity to set the tone to create a new impression that the course will
be important to their business education. Make the following points:
• This course is not a math course.
• State that you will be learning analytical skills for making business decisions.
• Explain that the focus will be on how statistics can be used in the functional areas of
business.
This book uses a systematic approach for meeting a business objective or solving a business
problem. This approach goes across all the topics in the book and most importantly can be used as
a framework in real world situations when students graduate. The approach has the acronym
DCOVA, which stands for Define, Collect, Organize, Visualize, and Analyze.
• Define the business objective or problem to be solved and then define the variables to be
studied.
• Collect the data from appropriate sources
• Organize the data

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• Visualize the data by developing charts
• Analyze the data by using statistical methods to reach conclusions.
To this, you can add C for Communicate which is critically important
You can begin by emphasizing the importance of defining your objective or problem. Then,
discuss the importance of operational definitions of variables to be considered and define
variable, data, and statistics.
Just as computers are used not just in the computer course, students need to know that
statistics is used not just in the statistics course. This leads you to a discussion of business
analytics in which data is used to make decisions. Make the point that analytics should be part of
the competitive strategy of every organization especially since “big data” meaning data collected
in huge volumes at very fast rates. needs to be analyzed.
Inform the students that there is an Excel Guide at the end of each chapter. Strongly
encourage or require students to read the Excel Guide at the end of this chapter so that they will
be ready to use Excel with this book.

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Chapter 1
You need to continue the discussion of the Define task by establishing the types of
variables. Be sure to discuss the different types carefully since the ability to distinguish between
categorical and numerical data will be crucial later in the course. Go over examples of each type
of variable and have students provide examples of each type. Then, if you wish, you can cover the
different measurement scales.
Then move on to the C of the DCOVA approach, collecting data. Mention the different
sources of data and make sure to cover the fact that data often needs to be cleaned of errors.
Then, you could spend some time discussing sampling, you may want to take a bit more time and
discuss the types of survey sampling methods and issues involved with survey sampling results.
The Consider This essay discusses the important issue of the use of Web-based surveys.
The chapter also introduces two continuing cases related to Managing Ashland
MultiComm Services and CardioGood Fitness that appear at the end of many chapters. The
Digital cases are introduced in this chapter also. In these cases, students visit Web sites
related to companies and issues raised in the Using Statistics scenarios that start each
chapter. The goal of the Digital cases is for students to develop skills needed to identify
misuses of statistical information. As would be the situation with many real world cases,
in Digital cases, students often need to sift through claims and assorted information in
order to discover the data most relevant to a case task. They will then have to examine
whether the conclusions and claims are supported by the data. (Instructional tips for using
the Managing Ashland MultiComm Services and Digital cases and solutions to the
Managing Ashland MultiComm Services and Digital cases are included in this Instructor’s
Solutions Manual.).
Make sure that students read the Excel Guide at the end of each chapter. Ways of
Working With Excel on pages 7 and 8 explains the different type of Excel instructions.
The Workbook instructions provide step-by-step instructions and live worksheets that
automatically update when data changes. The PHStat2 add-in instructions provide
instructions for using the PHStat2 add-in. Analysis ToolPak instructions provide
instructions for using the Analysis ToolPak, the Excel add-in package that is included
with many versions of Excel.

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Chapter 2
This chapter moves on to the organizing and visualizing steps of the DCOVA
framework. If you are going to collect sample data to use in Chapters 2 and 3, you can
illustrate sampling by conducting a survey of students in your class. Ask each student to
collect his or her own personal data concerning the time it takes to get ready to go to class
in the morning or the time it takes to get to school or home from school. First, ask the
students to write down a definition of how they plan to measure this time. Then, collect
the various answers and read them to the class. Then, a single definition could be
provided (such as the time to get ready is the time measured from when you get out of
bed to when you leave your home, recorded to the nearest minute). In the next class,
select a random sample of students and use the data collected (depending on the sample
size) in class when Chapters 2 and 3 are discussed.
Then, move on to the Organize step that involves setting up your data in an Excel
worksheet and develop tables to help you prepare charts and analyze your data. Begin
your discussion for categorical data with the example on p. 34 concerning how people
pay for purchases and other transactions. Show the summary table and then if you wish,
explain that you can sometimes organize the data into a two-way table that has one
variable in the row and another in the column.
Continue with organizing data (but now for numerical data) by referring to the
cost of a restaurant meal on p. 38. Show the simple ordered array and how a frequency
distribution, percentage distribution, or cumulative distribution can summarize the raw
data in a way that is more useful.
Now you are ready to tackle the Visualize step. A good way of starting this part of
the chapter is to display the following quote.
"A picture is worth a thousand words."
Students will almost certainly be familiar with Microsoft® Word and may have already used
Excel to construct charts that they have pasted into Word documents. Now you will be using
Excel to construct many different types of charts. Return to the purchase payment data previously
discussed and illustrate how a bar chart, pie, and doughnut chart can be constructed using Excel.
Mention the advantages and disadvantages of each chart. A good example is to show the data on

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incomplete ATM transactions on p. 49 and how the Pareto chart enables you to focus on the vital
few categories. If time permits, you can discuss the side-by-side bar chart for a contingency table.
To examine charts for numerical variables you can either use the restaurant data
previously mentioned, the retirement funds data, or data that you have collected from your class.
You may want to begin with a simple stem-and-leaf display that both organizes the data and
shows a bar type chart. Then move on to the histogram and the various polygons, pointing out the
advantages and disadvantages of each.
If time permits, you can discuss the scatter plot and the time-series plot for two numerical
variables. Otherwise, you can wait until you get to regression analysis. If you cover the time
series plot, you might also want to mention sparklines that are discussed in Section 2.6.
Also, if possible, you may want to discuss how multidimensional tables allow you to drill
down to individual cells of the table. You can follow this with further discussion of PivotTables
and Excel slicers that enable you to see panels for each variable being studied.
If the opportunity is available, we believe that it is worth the time to cover Section 2.7 on
Challenges in Organizing and Visualizing Variables. This is a topic that students very much enjoy
since it allows for a great deal of classroom interaction. After discussing the fundamental
principles of good graphs, try to illustrate some of the improper displays shown in Figures 2.26 –
2.28. Ask students what is “bad” about these figures. Follow up with a homework assignment
involving Problems 2.69 – 2.73 (USA Today is a great source).
You will find that the chapter review problems provide large data sets with numerous
variables. Report writing exercises provide the opportunity for students to integrate written and or
oral presentation with the statistics they have learned.
The Managing Ashland MultiComm Services case enables students to examine the use of
statistics in an actual business environment. The Digital case refers to the EndRun Financial
Services and claims that have been made. The CardioGood Fitness case focuses on developing a
customer profile for a market research team. The Choice Is Yours Follow-up expands on the
chapter discussion of the mutual funds data. The Clear Mountain State Student Survey provides
data collected from a sample of undergraduate students.
The Excel Guide for this and the remaining chapters are organized according to the
sections of the chapter. It is quite extensive since it covers both organizing and visualizing many
different graphs. The Excel Guide includes instructions for Workbook, PHStat2, and the Analysis
ToolPak.

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Chapter 3
This chapter on descriptive numerical statistical measures represents the initial
presentation of statistical symbols in the text. Students who need to review arithmetic and
algebraic concepts may wish to refer to Appendix A for a quick review or to appropriate texts
(see www.pearson.com) or videos (www.videoaidedinstruction.com). Once again, as with the
tables and charts constructed for numerical data, it is useful to provide an interesting set of data
for classroom discussion. If a sample of students was selected earlier in the semester and data
concerning student time to get ready or commuting time was collected (see Chapters 1 and 2), use
these data in developing the numerous descriptive summary measures in this chapter. (If they
have not been developed, use other data for classroom illustration.)
Discussion of the chapter begins with the property of central tendency. We have found
that almost all students are familiar with the arithmetic mean (which they know as the average)
and most students are familiar with the median. A good way to begin is to compute the mean for
your classroom example. Emphasize the effect of extreme values on the arithmetic mean and
point out that the mean is like the center of a seesaw -- a balance point. Note that you will return
to this concept later when you discuss the variance and the standard deviation. You might want to
introduce summation notation at this point and express the arithmetic mean in formula notation as
in Equation (3.1). (Alternatively, you could wait until you cover the variance and standard
deviation.) A classroom example in which summation notation is reviewed is usually worthwhile.
Remind the students again that Appendix A includes a review of arithmetic and algebra and
summation notation [or refer them to other text sources such as those found at www.pearson.com
or videos (see www. videoaidedinstruction.com)].
The next statistic to compute is the median. Be sure to remind the students that the
median as a measure of position must have all the values ranked in order from lowest to highest.
Be sure to have the students compare the arithmetic mean to the median and explain that this tells
us something about another property of data (skewness). Following the median, the mode can be
briefly discussed. Once again, have the students compare this result to those of the arithmetic
mean and median for your data set. If time permits, you can also discuss the geometric mean
which is heavily used in finance.
The completion of the discussion of central tendency leads to the second characteristic of
data, variability. Mention that all measures of variation have several things in common: (1) they
can never be negative, (2) they will be equal to 0 when all items are the same, (3) they will be
small when there isn't much variation, and (4) they will be large when there is a great deal of
variation.

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The first measure of variability to consider is the simplest one, the range. Be sure to point
out that the range only provides information about the extremes, not about the distribution
between the extremes. Point out that the range lacks one important ingredient, the ability to take
into account each data value. Bring up the idea of computing the differences around the mean, but
then return to the fact that as the balance point of the seesaw, these differences add up to zero. At
that point, ask the students what they can do mathematically to remove the negative sign for some
of the values. Most likely, they will answer by telling you to square them (although someone
may realize that the absolute value could be taken). Next, you may want to define the squared
differences as a sum of squares. Now you need to have the students realize that the number of
values being considered affects the magnitude of the sum of squared differences. Therefore, it
makes sense to divide by the number of values and compute a measure called the variance. If a
population is involved, you divide by N, the population size, but if you are using a sample, you
divide by n - 1, to make the sample result a better estimate of the population variance. You can
finish the development of variation by noting that since the variance is in squared units, you need
to take the square root to compute the standard deviation.
Another measure of variation that can be discussed is the coefficient of variation. Be sure
to illustrate the usefulness of this as a measure of relative variation by using an example in which
two data sets have vastly different standard deviations, but also vastly different means. A good
example is one that involves the volatility of stock prices. Point out that the variation of the price
should be considered in the context of the magnitude of the arithmetic mean. The final measure of
variation is the Z score. Point out that this provides a measure of variation in standard deviation
units. You can also say that you will return to Z scores in Chapter 6 when the normal distribution
will be discussed.
You are now ready to move on to the third characteristic of data, shape. Be sure to clearly
define and illustrate both symmetric and skewed distributions by comparing the mean and
median. You may also want to briefly mention the property of kurtosis which is the relative
concentration of values in the center of the distribution as compared to the tails. This statistic is
provided by Excel through an Excel function or the Analysis Toolpak.
Once these three characteristics have been discussed, you are ready to show how they can
be computed using Excel.
Now that these measures are understood, you can further explore data by computing the
quartiles, the interquartile range, the five number summary, and constructing a boxplot. You
begin by determining the quartiles. Reference here can be made to the standardized exams that
students have taken, and the quantile scores that they have received (97th percentile, 48th

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percentile, 12th percentile, etc.). Explain that the 1st and 3rd quartiles are merely two special
quantiles -- the 25th and 75th, that unlike the median (the 2nd quartile), are not at the center of the
distribution. Once the quartiles have been computed, the interquartile range can be determined.
Mention that the interquartile range computes the variation in the center of the distribution as
compared to the difference in the extremes computed by the range.
You can then discuss the five-number summary of minimum value, first quartile, median,
third quartile, and maximum value. Then, you construct the boxplot. Present this plot from the
perspective of serving as a tool for determining the location, variability, and symmetry of a
distribution by visual inspection, and as a graphical tool for comparing the distribution of several
groups. It is useful to display Figure 3.6 on page 118 that indicates the shape of the boxplot for
four different distributions. Then, use PHStat2 to construct a boxplot. Note that you can construct
the boxplot for a single group or for multiple groups.
If you desire, you can discuss descriptive measures for a population and introduce the
empirical rule and the Chebyshev rule.
If time permits, and you have covered scatter plots in Chapter 2, you can briefly discuss
the covariance and the coefficient of correlation as a measure of the strength of the association
between two numerical variables. Point out that the coefficient of correlation has the advantage as
compared to the covariance of being on a scale that goes from -1 to +1. Figure 3.9 on p. 127 is
useful in depicting scatter plots for different coefficients of correlation.
Once again, you will find that the chapter review problems provide large data sets with
numerous variables.
The Managing Ashland MultiComm Services case enables students to examine the use of
descriptive statistics in an actual business environment. The Digital case continues the evaluation
of the EndRun Financial Services discussed in the Digital case in Chapter 2. The CardioGood
Fitness case focuses on developing a customer profile for a market research team. More
Descriptive Choices Follow-up expands on the discussion of the mutual funds data. The Clear
Mountain State Student Survey provides data collected from a sample of undergraduate students.
The Excel Guide for the chapter includes instructions on using different Excel functions
to compute various statistics. Alternatively, you can use PHStat or the Analysis ToolPak to
compute a list of statistics. PHStat2 can be used to construct a boxplot.

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Chapter 4
The chapter on probability represents a bridge between the descriptive statistics already
covered and the topics of statistical inference, regression, time series, and business analytics to be
covered in subsequent chapters. In many traditional statistics courses, often a great deal of time is
spent on probability topics that are of little direct applicability in basic statistics. The approach in
this text is to cover only those topics that are of direct applicability in the remainder of the text.
You need to begin with a relatively concise discussion of some probability rules.
Essentially, students really just need to know that (1) no probability can be negative, (2) no
probability can be more than 1, and (3) the sum of the probabilities of a set of mutually exclusive
events adds to 1.0. Students often understand the subject best if it is taught intuitively with a
minimum of formulas, with an example that relates to a business application shown as a two-way
contingency table (see the Using Statistics example). If desired, you can use the Excel Workbook
instructions or PHStat2 to compute probabilities from the contingency table.
Once these basic elements of probability have been discussed, if there is time and you
desire, conditional probability and Bayes’ theorem (an online topic) can be covered. The
Consider This concerning email SPAM is a wonderful way of helping students realize the
application of probability to everyday life. Be aware that in a one-semester course where time is
particularly limited, these topics may be of marginal importance. The Digital case in this chapter
extends the evaluation of the EndRun Financial Services to consider claims made about various
probabilities. The CardioGood Fitness, More Descriptive Choices Follow-up, and Clear Mountain
State Student Survey each involve developing contingency tables to be able to compute and
interpret conditional and marginal probabilities.

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