Tài liệu Thống kê statistical techniques in business and economics 17th by wathen

Statistical
Techniques
in Business
& Economics
Seventeenth Edition

LIND
MARCHAL
WATHEN


Statistical Techniques in

BUSINESS &
ECONOMICS




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Statistical Techniques in

BUSINESS &
ECONOMICS
SEVENTEENTH EDITION

DOUGLAS A. LIND
Coastal Carolina University and The University of Toledo

WILLIAM G. MARCHAL
The University of Toledo

SAMUEL A. WATHEN
Coastal Carolina University


STATISTICAL TECHNIQUES IN BUSINESS & ECONOMICS, SEVENTEENTH EDITION
Published by McGraw-Hill Education, 2 Penn Plaza, New York, NY 10121. Copyright © 2018 by
McGraw-Hill Education. All rights reserved. Printed in the United States of America. Previous editions
© 2015, 2012, and 2010. No part of this publication may be reproduced or distributed in any form or

by any means, or stored in a database or retrieval system, without the prior written consent of McGrawHill Education, including, but not limited to, in any network or other electronic storage or transmission,
or broadcast for distance learning.
Some ancillaries, including electronic and print components, may not be available to customers outside
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Library of Congress Cataloging-in-Publication Data

Names: Lind, Douglas A., author. | Marchal, William G., author. | Wathen,
  Samuel Adam. author.
Title: Statistical techniques in business & economics/Douglas A. Lind,
  Coastal Carolina University and The University of Toledo, William G.
  Marchal, The University of Toledo, Samuel A. Wathen, Coastal Carolina University.
Other titles: Statistical techniques in business and economics
Description: Seventeenth Edition. | Dubuque, IA : McGraw-Hill Education,
  [2017] | Revised edition of the authors’ Statistical techniques in
  business & economics, [2015]
Identifiers: LCCN 2016054310| ISBN 9781259666360 (alk. paper) | ISBN
  1259666360 (alk. paper)
Subjects: LCSH: Social sciences—Statistical methods. |
  Economics—Statistical methods. | Commercial statistics.
Classification: LCC HA29 .M268 2017 | DDC 519.5—dc23 LC record available at
  />The Internet addresses listed in the text were accurate at the time of publication. The inclusion of a
website does not indicate an endorsement by the authors or McGraw-Hill Education, and McGraw-Hill
Education does not guarantee the accuracy of the information presented at these sites.
mheducation.com/highered


D E D I CATI O N
To Jane, my wife and best friend, and our sons, their wives, and our
grandchildren: Mike and Sue (Steve and Courtney), Steve and Kathryn
(Kennedy, Jake, and Brady), and Mark and Sarah (Jared, Drew, and Nate).
Douglas A. Lind
To Oscar Sambath Marchal, Julian Irving Horowitz, Cecilia Marchal
Nicholson and Andrea.
William G. Marchal
To my wonderful family: Barb, Hannah, and Isaac.
Samuel A. Wathen

A

NOTE

FROM

THE

AUTHOR

S

Over the years, we received many compliments on this text and understand that it’s a
favorite among students. We accept that as the highest compliment and continue to
work very hard to maintain that status.
The objective of Statistical Techniques in Business and Economics is to provide
students majoring in management, marketing, finance, accounting, economics, and
other fields of business administration with an introductory survey of descriptive and inferential statistics. To illustrate the application of statistics, we use many examples and
­exercises that focus on business applications, but also relate to the current world of the
college student. A previous course in statistics is not necessary, and the mathematical
requirement is first-year algebra.
In this text, we show beginning students every step needed to be successful in
a basic statistics course. This step-by-step approach enhances performance, accelerates preparedness, and significantly improves motivation. Understanding the
concepts, seeing and doing plenty of examples and exercises, and comprehending
the application of statistical methods in business and economics are the focus of
this book.
The first edition of this text was published in 1967. At that time, locating relevant
business data was difficult. That has changed! Today, locating data is not a problem.

The number of items you purchase at the grocery store is automatically recorded at
the checkout counter. Phone companies track the time of our calls, the length of calls,
and the identity of the person called. Credit card companies maintain information on
the number, time and date, and amount of our purchases. Medical devices automatically monitor our heart rate, blood pressure, and temperature from remote locations.
A large amount of business information is recorded and reported almost instantly.
CNN, USA Today, and MSNBC, for example, all have websites that track stock prices
in real time.
Today, the practice of data analytics is widely applied to “big data.” The practice
of data analytics requires skills and knowledge in several areas. Computer skills are
needed to process large volumes of information. Analytical skills are needed to
evaluate, summarize, organize, and analyze the information. Critical thinking skills
are needed to interpret and communicate the results of processing the
information.
Our text supports the development of basic data analytical skills. In this edition,
we added a new section at the end of each chapter called Data Analytics. As you
work through the text, this section provides the instructor and student with opportunities to apply statistical knowledge and statistical software to explore several business environments. Interpretation of the analytical results is an integral part of these
exercises.
A variety of statistical software is available to complement our text. Microsoft Excel
includes an add-in with many statistical analyses. Megastat is an add-in available for
Microsoft Excel. Minitab and JMP are stand-alone statistical software available to download for either PC or MAC computers. In our text, Microsoft Excel, Minitab, and Megastat
are used to illustrate statistical software analyses. When a software application is presented, the software commands for the application are available in Appendix C. We use
screen captures within the chapters, so the student becomes familiar with the nature of
the software output.
Because of the availability of computers and software, it is no longer necessary to
dwell on calculations. We have replaced many of the calculation examples with interpretative ones, to assist the student in understanding and interpreting the statistical results.
In addition, we place more emphasis on the conceptual nature of the statistical topics.
While making these changes, we still continue to present, as best we can, the key concepts, along with supporting interesting and relevant examples.

vi

WHAT’S NEW IN THE SEVENTEENTH EDITION?
We have made many changes to examples and exercises throughout the text. The section on “Enhancements” to our text details them. The major change to the text is in
­response to user interest in the area of data analytics. Our approach is to provide instructors and students with the opportunity to combine statistical knowledge, computer
and statistical software skills, and interpretative and critical thinking skills. A set of new
and revised exercises is included at the end of chapters 1 through 18 in a section titled
“Data Analytics.”
In these sections, exercises refer to three data sets. The North Valley Real Estate
sales data set lists 105 homes currently on the market. The Lincolnville School District
bus data lists information on 80 buses in the school district’s bus fleet. The authors designed these data so that students will be able to use statistical software to explore the
data and find realistic relationships in the variables. The Baseball Statistics for the 2016
season is updated from the previous edition.
The intent of the exercises is to provide the basis of a continuing case analysis. We
suggest that instructors select one of the data sets and assign the corresponding exercises as each chapter is completed. Instructor feedback regarding student performance
is important. Students should retain a copy of each chapter’s results and interpretations
to develop a portfolio of discoveries and findings. These will be helpful as students
progress through the course and use new statistical techniques to further explore the
data. The ideal ending for these continuing data analytics exercises is a comprehensive
report based on the analytical findings.
We know that working with a statistics class to develop a very basic competence in
data analytics is challenging. Instructors will be teaching statistics. In addition, instructors will be faced with choosing statistical software and supporting students in developing or enhancing their computer skills. Finally, instructors will need to assess student
performance based on assignments that include both statistical and written components. Using a mentoring approach may be helpful.
We hope that you and your students find this new feature interesting and engaging.



vii


H OW A R E C H A P TE RS O RGA N I Z E D TO E N GAG E

DESCRIBING
DATA:
STU D E NTS A N D PRO M OTE
LE
ADISPLAYING
RN I NAND
G?EXPLORING DATA

95

INTRODUCTION
Chapter 2 began our study of descriptive statistics. In order to transform raw or ungrouped data into a meaningful form, we organize the data into a frequency distribution.
We present the frequency distribution in graphic form as a histogram or a frequency
polygon.
This
allowsrecently
us to visualize
data tend
to cluster,
the for
largest
and the
MERRILL
LYNCH
completedwhere
a studythe
of online
investment
portfolios
a sample

Each chapter begins with a set of
smallest
values,
and
general in
shape
of the
data. these data into a frequency
of clients.
For the
70the
participants
the study,
organize
)
distribution.
(See
and LO2-3.
In Chapter
3, Exercise
we first 43
computed
several
measures of location, such as the mean,
learning objectives designed to promedian, and mode. These measures of location allow us to report a typical value in the
vide focus for the chapter and motivate
set of observations. We also computed several measures of dispersion, such as the
student learning. These objectives, lorange, variance, and standard deviation. These measures of dispersion allow us to deLEARNING OBJECTIVES
cated in the margins next to the topic,
scribe the variation or the spread in a set of observations.

When you have completed this chapter, you will be able to:
We continue our study of descriptive statistics in this chapter. We study (1) dot plots,
indicate what the student should be
Summarize
qualitative(3)
variables
with frequency
tables. and statistics
(2)LO2-1
stem-and-leaf
displays,
percentiles,
and (4)and
boxrelative
plots.frequency
These charts
able to do after completing each secgive
us additional
insight into
are concentrated as well as the general
LO2-2
Display a frequency
tablewhere
using athe
bar values
or pie chart.
tion in the chapter.
shape of the data. Then we consider bivariate data. In bivariate data, we observe two
LO2-3 Summarize quantitative variables with frequency and relative frequency distributions.
variables for each individual or observation. Examples include the number of hours a

LO2-4 studied
Display aand
frequency
distribution
using
or frequency
student
the points
earned
ona histogram
an examination;
if a polygon.
sampled product meets
quality specifications and the shift on which it is manufactured; or the amount of electricity used in a month by a homeowner and the mean daily high temperature in the region
theshows
month. how
These the
charts
and graphs
provide
useful
as to
weause
business
A representative exercise opens the chapter for
and
chapter
content
can
be insights

applied
real-world
analytics to enhance our understanding of data.
situation.

Chapter Learning Objectives

Source: © rido/123RF

Chapter Opening Exercise

19

DESCRIBING DATA: FREQUENCY TABLES, FREQUENCY DISTRIBUTIONS, AND GRAPHIC PRESENTATION

LO4-1
Construct and interpret a
dot plot.

Introduction to the Topic

DOT PLOTS

INTRODUCTION
Recall for the Applewood
Auto Group data, we summarized the profit earned on the
The
United
States automobile
retailing industry

highlyclasses.
competitive.
It is dominated
by
180 vehicles sold
with
a frequency
distribution
using iseight
When
we orgamegadealerships
that ownwe
andlost
operate
or more
franchises,
over 10,000
Each chapter starts with a review of
nized the data into
the eight classes,
the 50
exact
value
of the employ
observations.
A
people, and generate several billion dollars in annual sales. Many of the top dealerships
dot plot, on the other hand, groups
theowned
datawith

as shares
little as
possible,
andYork
weStock
do not
lose
the important concepts of the previare publicly
traded
on the New
Exchange
the identity
of an individual observation.
To develop
dot plot, we was
display
a dot(ticker
for
Lin66360_ch02_018-050.indd
18
or NASDAQ. In 2014,
the largestamegadealership
AutoNation
ous chapter and provides a link to the
symbol AN),
followedline
by Penske
Auto Group
(PAG), Group
1 Automotive,

each observation along a horizontal
number
indicating
the possible
values
of the
Inc. (ticker symbol
GPI),
and the privately
Van Tuyl
material in the current chapter. This
data. If there are identical observations
or the
observations
areowned
too close
toGroup.
be shown
These large corporations use statistics and analytics to summarize
individually, the dots are “piled”
on
top
of
each
other.
This
allows
us
to
see

theAsshape
step-by-step approach increases comand analyze data and information to support their decisions.
an exof the distribution, the value about
which
the at
data
tend to cluster,
and Itthe
largest
and
ample, we
will look
the Applewood
Auto group.
owns
four dealerprehension by providing continuity
shipsare
andmost
sells auseful
wide range
of vehicles.
the popular
smallest observations. Dot plots
for smaller
dataThese
sets,include
whereas
histoacross the concepts.
brands
Kia sets.

and Hyundai,
BMW and
sedans
luxury
grams tend to be most usefulKorean
for large
data
An example
willVolvo
show
howand
to conand a full line of Ford and Chevrolet cars and trucks.
struct and interpret dot plots.SUVs,
Ms. Kathryn Ball is a member of the senior management team at
Applewood Auto Group, which has its corporate offices adjacent to Kane
Motors. She is responsible for tracking and analyzing vehicle sales and
the profitability of those vehicles. Kathryn would like to summarize the profit earned on
the vehicles sold with tables, charts, and graphs that she would review monthly. She
E X A M P L E wants to know the profit per vehicle sold, as well as the lowest and highest amount of
profit. She is also interested in describing the demographics of the buyers. What are
The service departments
at many
Tionesta
Ford
and Sheffield
their ages? How
vehicles
haveLincoln
they previously
purchasedMotors

from oneInc.,
of thetwo
Appleof the four Applewood
Auto Group
were
both open 24 days last
wood dealerships?
What typedealerships,
of vehicle did they
purchase?
The Applewood
Auto Group
operates four
dealerships:
month. Listed below
is the number
of vehicles
serviced
last month at the two

Source: © Justin Sullivan/Getty Images

Example/Solution
After important concepts are introduced,
a solved example is given. This example
provides a how-to illustration and shows
a relevant business application that
helps students answer the question,
“How can I apply this concept?”

dealerships. Construct
dot
plots
andsells
report
summary
statistics
to compare the
• Tionesta
Ford
Lincoln
Ford and
Lincoln cars
and trucks.
• Olean Automotive Inc. has the Nissan franchise as well as the General Motors
two dealerships.
brands of Chevrolet, Cadillac, and GMC Trucks.
• Sheffield Motors Inc. sells Buick, GMC trucks, Hyundai, and Kia.
• Kane Motors offers
the Chrysler,
Dodge, and Jeep line as well as BMW and Volvo.
Tionesta
Ford Lincoln

Monday

month, Ms. Ball
collects data
from each
of the four dealerships

Tuesday Every
Wednesday
Thursday
Friday
Saturday

and enters them into an Excel spreadsheet. Last month the Applewood

23
30
CHAPTER294

106

33Auto Group27
28 at the 39
26
sold 180 vehicles
four dealerships.
A copy of the first
32few observations
28 appears 33
35 variables
32collected include:
to the left. The
25 • Age—the
36 age of the buyer
31 at the 32
27
time of the purchase.

32 • Profit—the
35 amount earned
37
36 dealership
30 on the sale of each
by the

35

vehicle.

calculate quartiles. Excel
and Excel
2016 offer
both
The
Excel function,
• 2013
Location—the
dealership
where
themethods.
vehicle was
purchased.
Quartile.exc, will result
the same
answer sedan,
as Equation
4–1.
The or

Excel
function, Quar •in Vehicle
type—SUV,
compact,
hybrid,
truck.
•Excel
tile.inc, will result in the
Method answers.
Previous—the
number of  vehicles previously purchased at any of the

Self-Reviews
Self-Reviews are interspersed
throughout each chapter and
follow Example/Solution sections. They help
students monLin66360_ch04_094-131.indd
95
itor their progress and provide
immediate reinforcement for
that particular technique. Answers are in Appendix E.

four Applewood dealerships by the consumer.

SELF-REVIEW

The entire data set is available at the McGraw-Hill website (www.mhhe
.com/lind17e) and in Appendix A.4 at the end of the text.

4–2

The Quality Control department of Plainsville Peanut Company is responsible for checking

CONSTRUCTING FREQUENCY TABLES

LO2-1
the weight of the 8-ounce jar of peanut butter. The weights of a sample of nine jars proSummarize qualitative
duced last hour are:
Recall from Chapter 1 that techniques used to describe a set of data are called descrip1/10/17 7:41 PM
variables with frequency
tive statistics. Descriptive statistics organize data to show the general pattern of the
and relative frequency
7.72where
7.8values
7.86tend 7.90
7.94
7.97
8.06
8.09
data, 7.69
to identify
to concentrate,
and to expose
extreme or unusual
tables.

data values. The first technique we discuss is a frequency table.

(a) What is the median weight? 
(b) Determine

the weights
corresponding
first anddata
thirdinto
quartiles. 
FREQUENCY
TABLE
A groupingtoofthe
qualitative
mutually exclusive and
collectively exhaustive classes showing the number of observations in each class.

EXERCISES
11.

viii

Determine the median and the first and third quartiles in the following data.
46

Lin66360_ch02_018-050.indd 19

12.

47

49

49

51

53

54

54

55

55

59

Determine the median and the first and third quartiles in the following data.

1/6/17 4:52 AM

1/6/17 4:52 AM


(c)

Are the events in part (a)(i) complementary or mutually ex

The probability of passing both is .50. What is the probability of passing at least one?

21. The aquarium at Sea Critters Depot contains 140 fish. Eighty of these fish are green

The General Rule of Addition

swordtails (44 female and 36 male) and 60 are orange swordtails (36 female and
24 males). A fish is randomly captured from the aquarium:  
a. What is the probability the selected
fish is a green
  may not be mutually exclu
The outcomes
of answordtail?
experiment
b. What is the probability the selected
is male?   selected a sample of 200 tourists wh
Tourist fish
Commission
c. What is the probability the selected
fishsurvey
is a male
green swordtail?
year. The
revealed
that 120 tourists went to Disney
d. What is the probability the selected fish is either a male or a green swordtail?  
Gardens near Tampa. What is the probability that a person
22. A National Park Service survey of visitors to the Rocky Mountain region revealed
or Busch
Gardens?
If the
that 50% visit Yellowstone Park,World
40% visit
the Tetons,
and 35%

visitspecial
both. rule of addition is use
a touristwill
who
to Disney
Worldattractions?
is .60, found by 120/200
a. What is the probability a vacationer
visitwent
at least
one of these
tourist going to Busch Gardens is .50. The sum of these p
b. What is the probability .35 called?
however,
that this probability cannot be greater than 1. The
c. Are the events mutually exclusive?
Explain.

Statistics in Action

STATISTICS IN ACTION

ists visited both attractions and are being counted twice! A c
revealed that 60 out of 200 sampled did, in fact, visit both a

that you believe that at

bility of visiting both. Thus:

A SURVEY OF PROBABILITY CONCEPTS

145
LO5-4
To answer our question, “What is the probability a se
If you wish to get some
Calculate probabilities
Disney
World
or
Busch
Gardens?”
(1)
add
the
probability
attention at the next gathStatistics in Action articles are
usingscattered
the rules of throughWorld and the probability he or she visited Busch Garden
ering
you
attend,
announce
multiplication.
out the text, usually about two per chapter. They
In this section, we discuss the rules for computing the likelihood that two events both

RULES OF MULTIPLICATION
TO CALCULATE PROBABILITY

happen, or their joint probability.
example, 16%

of the
2016 tax returns were preprovide unique, interesting applications and hisP(Disney)For
= .60
P(Busch)
= .50
least two people present
P(Disney
Busch) What
= P(Disney)
+ P(Busch) − P(bo
pared by H&R Block and 75% of those returns
showedor
a refund.
is the likelihood
torical insights in the field of statistics.
weretax
born
on the
same
a person’s
form
was
prepared by H&R Block and the person received a refund?

= .60 + .50 − .30 = .80

64

Definitions

Definitions of new terms or terms unique to
the study of statistics are set apart from the
text and highlighted for easy reference and
review. They also appear in the Glossary at
the end of the book.

Formulas
Formulas that are used for the first time are
boxed and numbered for reference. In addition, a formula card is bound into the back of
the text that lists all the key formulas.

Exercises

date—thatillustrate
is, the same
Venn diagrams
this as the intersection of two events. To find the likelihood of
day of
the year butwe
notuse the rules of
When
two events
both
the probability
two events
happening,
multiplication.
There
areoccur,
two rules

of multipli- is called
necessarily
same
ability
cation: the
specialthe
rule
andyear.
the general
rule.(.30) that a tourist visits both attractions is an examp
If there are 30 people in
the room,
the of
probability
of
Special
Rule
Multiplication
a duplicate is .706. If there
The special
rule
of
multiplication
are
60 people3in the room, requires that two events A and B are independent.
CHAPTER
P(Disney
and Busch)
= .30
Two events

are independent
if the
occurrence
of one
event does not alter the probabilthe probability
is .994 that
ity of theatoccurrence
of the
least two people
shareother
the event.

same birthday. With as few
INDEPENDENCE
The
one of
event
has nounemployment
effect on therates? 
probability of
a. What
isoccurrence
the arithmeticofmean
the Alaska
as 23 people
the
chances
JOINT
PROBABILITY
probability

that measures the likelihood two or more
the
occurrence
of
another
event.
Find
theAthat
median
are even,b.that
is .50,
at and the mode for the unemployment rates. 
events will happen
concurrently.
Compute
least two c.people
sharethe
thearithmetic mean and median for just the winter (Dec–Mar) months.
Is it much different? 
birthday.
Hint:
To
Onesame
way
to
think
about
independence
to assume
events A

and Bfor
occur
22.
Big Orange Trucking is is
designing
an that
information
system
use at
in differ“in-cab”
ent times.
For
example,
when
event
B occurs
after
event
A
occurs,
does
A have any
this, find
the
communications.
It must
summarize
data
from
eight sites

a region
So compute
the
general
rule
of addition,
which
is used
to
compute
thethroughout
probability
ofeffect
twoto
on
the likelihood
that
event
Bexclusive,
occurs?
Ifis:
the
answer
no, then measure
A and B of
are
independent
typical
conditions.
Compute

an is
appropriate
central
location for
probability
everyone
was
events
that
are describe
not
mutually
the
variables
wind
direction,
temperature,
and
pavement.
events. To
illustrate
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The outcome of a coin
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Rostislav Glinsky/Shutterstock.com
toss (head
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City
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twothis
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events A and B,The
the probability
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will both
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is that are n
Venn
events
in your
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GENERAL
RULE
OF
ADDITION
P(A or following
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P(B)diagram
− P(A and B) two[5–4]
West

89the
found by multiplying Anniston,
the twoALprobabilities.
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illustrate
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that and
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Atlanta,
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86
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is written symbolically
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Augusta, GA

Southwest

92

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For the expression
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or suggests that A may
Birmingham,
91 occur or B may
Dry occur.
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also includes
the
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SPECIAL
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If we compare the
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determining if the events are mutually exclusive. If the events are mutually exclusive, then
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wise, we must account for the joint probability and use the general rule of addition.

Software Solution

Lin66360_ch05_132-174.indd 144

Exercises are included
after sec-147 E X E R C I S E SE X A M P L E
Lin66360_ch05_132-174.indd
1/10/17 7:41 PM
47–52, do the following:
What isFor
theExercises
probability
that a card chosen at random from a standard deck of cards
tions within the chapter and at
E
X
A
M
P
L
E
a. Compute
sample variance.
will be either
a king orthe
a heart?
the end of the chapter. Section
b. Determine the sample standard deviation.
Table 2–4 on page 26 shows the profit on the sales of 180 vehicles at Applewood

47.
Consider
these
values
a sample: 7, 2, 6, 2, and 3. 
exercises cover the material studAuto Group. Determine the mean and the median selling price.
48.
SOLU
T IThe
O Nfollowing five values are a sample: 11, 6, 10, 6, and 7.
ied in the section. Many exercises
49.
Dave’s Automatic Door, referred to in Exercise 37, installs automatic garage
openers.
on a sample,
times, in minutes,
required
We may bedoor
inclined
to addBased
the probability
of afollowing
king andare
thethe
probability
of a heart.
But thisto
have data files available to import
S
O

L
U
T IIfdoor
Owe
N openers:
install 10
28, 32,
44, 40,
54, 38, 32,
and
creates a problem.
do that, the
king24,
of 46,
hearts
is counted
with
the42. 
kings and also
into statistical software. They are
Theifsample
of eight
companies
in the aerospace
industry,
to in
with the50.
hearts.
So,
we

simply
addmodal
the probability
king (there
are 4referred
in aindeck
ofExer52
The mean,
median,
and
amountsofofa profit
are reported
the following
cise
38, was of
surveyed
as
to their
return
on
investment
last year.
The results
are
indicated with the FILE icon.
cards) to the
probability
a heart
(there
are shot).

13 in a(Reminder:
deck
of 52The
cards)
and report
17
output
(highlighted
in the
screen
instructions
tothat
create
the
10.6, 12.6, 14.8, 18.2, 12.0, 14.8, 12.2, and 15.6.
out
of
52
cards
meet
the
requirement,
we
have
counted
the
king
of
hearts
twice.

We
output
appear
in
the
Software
Commands
in
Appendix
C.)
There
are
180
vehicles
­Answers to the odd-numbered
51.
The Houston, Texas, Motel Owner Association conducted a survey regarding
need to subtract
1 card
17
theListed
king below
ofbehearts
is counted
once.
inweekday
the study,
sofrom
using
aincalculator

would
tedious
and prone
tobusiness-class
error. Thus,
motel
ratesthe
the so
area.
is the
room
rateonly
for
exercises are in Appendix D.
there are 16 cards that are either hearts or kings. So the probability is 16/52 = .3077.
We can use a statistical software package to find many measures of location.

guests for a sample of 10 motels.

Card

$101

$97

$103

$110

Probability

$78

$87

$101

$80

Explanation

$106

$88

A consumer
organization is
concerned
credit card debt. A
P(A)watchdog
= 4/52
4 kings
in a deckabout
of 52 cards
survey of 10 young
debt
of more
than
$2,000
Heart

P(B) adults=with
13/52credit card13
hearts
in a deck
of 52
cards showed they
paid
an averageP(A
of and
justB)over
$100 per month
balances.
Listed below
King
of Hearts
= 1/52
1 king ofagainst
hearts intheir
a deck
of 52 cards
are the amounts each young adult paid last month.

52.King

Computer Output

$110
$126
$103
$93

$99
$113
$87
The text includes many software examples, using
­Excel, MegaStat®, and Minitab. The software results are
LO3-5 for a particular
illustrated in the chapters. Instructions
INTERPRETATION AND USES
software example are in AppendixExplain
C. and apply
Chebyshev’s theorem
OF THE STANDARD DEVIATION

and the Empirical Rule.

Lin66360_ch05_132-174.indd 145



STATISTICS IN ACTION
Most colleges report the
“average class size.” This
information can be mislead-

$101

$109

$100

The standard deviation is commonly used as a measure to compare the spread in two
or more sets of observations. For example, the standard deviation of the biweekly
amounts invested in the Dupree Paint Company profit-sharing plan is computed1/10/17
to be 7:41 PM
$7.51. Suppose these employees are located in Georgia. If the standard deviation for a
group of employees in Texas is $10.47, and the means are about the same, it indicates
that the amounts invested by the Georgia employees are not dispersed as much as
those in Texas (because $7.51 < $10.47). Since the amounts invested by the Georgia
employees are clustered more closely about the mean, the mean for the Georgia emix
ployees is a more reliable measure than the mean for the Texas group.

Chebyshev’s Theorem
We have stressed that a small standard deviation for a set of values indicates that these


she estimates that the probability is .025 that an applicant will not be able to repay
his or her installment loan. Last month she made 40 loans.
a. What is the probability that three loans will be defaulted?
b. What is the probability that at least three loans will be defaulted?
34. Automobiles arrive at the Elkhart exit of the Indiana Toll Road at the rate of two per
minute. The distribution of arrivals approximates a Poisson distribution.
a. What is the probability that no automobiles arrive in a particular minute?
b. What is the probability that at least one automobile arrives during a particular
minute?
35. It is estimated that 0.5% of the callers to the Customer Service department of Dell
Inc. will receive a busy signal. What is the probability that of today’s 1,200 callers at
least 5 received a busy signal?
36. In the past, schools in Los Angeles County have closed an average of 3 days each
year for weather emergencies. What is the probability that schools in Los Angeles
County will close for 4 days next year?

H OW DO E S TH I S TE X T R E I N FO RC E
STU D E NT LE A R N I N G?

BY C H A P TE R

CHAPTER SUMMARY
348

I. A random
CHAPTER
10variable is a numerical value determined by the outcome of an experiment.

244
168

1. The probability of making a Type
error is equal to the level of significance. (6–1)
μ =I Σ[xP(x)]
CHAPTER
7 probability is designated by the Greek letter α.
2. This
B. The
variance is equal to:
CHAPTER
5 II error occurs when a false null hypothesis is not rejected.
B. A Type
= Σ[(x
− μ)2isP(x)]
1. The probability of making aσ2Type

II error
designated by the Greek letter β. (6–2)

II. A probability distribution is a listing of all possible outcomes of an experiment and the
probability associated with each outcome.
A. A
discrete
distribution
assume only
The
majorprobability
characteristics
of the can
t distribution
are:certain values. The main features are:
1.
of the probabilities
1. The
It is asum
continuous
distribution.is 1.00.
2.
The
probability
of
a
particular
outcome
is
between

0.00 and 1.00.
2. It is mound-shaped and symmetrical.
3.
outcomes
are mutually
exclusive.
3. The
It is flatter,
or more
spread out,
than the standard normal distribution.
B. A
continuous
distribution
can
assume
an
infinite
number
of valuesofwithin
a specific
range.
4. There is a family of t distributions, depending on the number
degrees
of freedom.
III.
mean
and types
variance
of a probability

distribution
V. The
There
are two
of errors
that can occur
in a testare
of computed
hypothesis.as follows.
A.
mean
is equal
to:when a true null hypothesis is rejected.
A. The
A Type
I error
occurs

Chapter Summary
Each chapter contains a brief summary
of the chapter material, including vocabulary, definitions, and critical formulas.

Pronunciation Key
This section lists the mathematical symbol,
its meaning, and how to pronounce it. We
believe this will help the student retain the
meaning of the symbol and generally enhance course communications.

Chapter Exercises

2. The likelihood
of a Type
II errorthe
must
be calculated
comparing
thelimits
hypothesized
68. In establishing
warranties
on HDTVs,
manufacturer
wants
to set the
so that few
distribution to an alternate distribution based on sample results.

need repair at the manufacturer’s expense. On the other hand, the warranty period
P R O N U N C I A T I O Nwill
K
EY
must be long enough to make the purchase attractive to the buyer. For a new HDTV, the

mean number of MEANING
months until repairs are needed is 36.84 with a standard
deviation of
SYMBOL
PRONUNCIATION

P R O N U N C I A T I O N3.34Kmonths.

E Y Where should the warranty limits be set so that only 10% of the HDTVs

Lin66360_ch06_175-208.indd 202

CHAPTER
CHAPTER

Generally, the end-of-chapter exercises
are the most challenging and integrate
the chapter concepts. The answers and
worked-out solutions for all oddnumbered exercises are in Appendix D
at the end of the text. Many exercises
are noted with a data file icon in the
margin. For these exercises, there are
data files in Excel format located on the
text’s website, www.mhhe.com/Lind17e.
These files help students use statistical
software to solve the exercises.

Data Analytics
The goal of the Data Analytics sections is to develop analytical skills.
The exercises present a real world
context with supporting data. The data
sets are printed in Appendix A and
available to download from the text’s
website www.mhhe.com/Lind17e. Statistical
software is required to analyze the data
and respond to the exercises. Each data
set is used to explore questions and discover findings that relate to a real world
context. For each business context, a

story is uncovered as students progress
from chapters one to seventeen.

x

DATA ANA

P(A)
Probability of A
P of A
need repairs at the manufacturer’s expense?
SYMBOL
MEANING
P(∼A)
Probability
not A
P of not Aselects
69.
DeKorte Tele-Marketing
Inc. of
is considering
purchasing a machine thatPRONUNCIATION
randomly
H0 and
hypothesis
and B)
automaticallyProbability
dialsNull
telephone
numbers.

DeKorte Tele-MarketingHmakes
P(A
of A and
B
Psub
of zero
Amost
and Bof its
calls
so of
calls
toBbusiness phones are wasted. The
manufacturer
of
H1 or
Alternate
H Psub
P(A
B) during the evening,
Probability
Ahypothesis
or
of one
A or B
the machine claims that its programming reduces the calling to business phones to 15%
α/2| B)
Two-tailed
significance
level
Alpha

divided
P(A 
Probability
of the
A given
B has
P of A
given by
Bthe2
of all calls. To test
this claim,
director
ofhappened
purchasing at DeKorte programmed
xP
Limit of of
then sample
mean r at a time
x bar
Permutation
items selected
Pnr sub c
n cr machine to select a sample of 150 phone numbers. What is the likelihood that more
numbersofselected
aremean
thoser of
assuming
the manuAssumed
population
muCnr

sub zero
μC than 30 of the phone
Combination
n items selected
at abusinesses,
time
n 0r
facturer’s claim is correct?
70. A carbon monoxide detector in the Wheelock household activates once every 200 days
average.
E X E R ConI S
E S Assume this activation follows the exponential distribution. What is the
E X E R Cprobability
I S E S that:
There will
alarm
within
the nextthe
60 mean
days? gross income of plumbers in the Salt
25. a.
According
tobe
theanlocal
union
president,
47. b.
The
department
atthe

Pepsico
plans to survey
about a newly
Atmarketing
least
400research
days will
pass
before
nextdistribution
alarm?
Lake
City area
follows
the
normal
probability
with ateenagers
mean of $45,000
and a
developed
soft
drink.
Each
will250
be
asked
to compare
with his for
or her

favorite
soft drink.
c.
It will be
between
and
days until
the nextitwarning?
standard
deviation
of150
$3,000.
A recent
investigative
reporter
KYAK
TV found,
for a
a.
What
is
the
experiment?
d.
Find the
median
time until
next
activation.
sample

of 120
plumbers,
thethe
mean
gross
income was $45,500. At the .10 significance
b.
What
one
event?thethat
71. “Boot
time”
(thepossible
time between
appearance
the Bios
screen
toto
the
first file that
is
level,
is itisreasonable
to conclude
the meanofincome
is not
equal
$45,000?
Deter48. loaded
The

of timesona Eric
particular
event
occurred
in the past
is divided
by the number
of
in Windows)
Mouser’s
personal
computer
follows
an exponential
distribuminenumber
the
p-value.
occurrences.
What
is
this
approach
to
probability
called?
tion
with
a
mean
of

27
seconds.
What
is
the
probability
his
“boot”
will
require:
Rutter Nursery Company packages its pine bark mulch in 50-pound bags. From a
26.
49. a.
The
probability
the cause
and the cure
for allthat
cancers
will be discovered
before
the
Less
than 15
seconds?
long
history,
thethat
production
department

reports
the distribution
of the bag
weights
year
2020
isnormal
.20.
What
viewpoint
of the
probability
does
this statement
illustrate?process is
b.
More
than
60 seconds?
follows
the
distribution
and
standard
deviation
of the packaging
Berdine’s
Chicken
Factory
several

stores
in the
Head,manager,
South Carolina,
50. c.
Between
30bag.
and
45the
seconds?
3 pounds
per
At
end of has
each
day, Jeff
Rutter,
theHilton
production
weighs
c. In thed.
dialog
box,
that thebelow
range of
Variable
1 isweight
area.
When
interviewing

applicants
for
server
positions,
the
owner
like to inWhat
isindicate
the
point
which
only
10%
ofofthe
boots
occur?
10
bags
and
computes
the
mean
the
sample.
Below
arewould
the weights
of
from A1
to A6 information

and Variable 2 from
to B7,
the Hypotheclude
onB1
the
of tip a server
to earn
per
check popula(or bill).
72.
between
visits
toamount
aAlpha
U.S.is emergency
roomcan
forexpect
a member
of the
general
10 bags
fromistoday’s
production.
sizedThe
Meantime
Difference
0, click
Labels,
0.05,
A Output

study
of 500
recent
indicated
server
theWhat
following
amounts
in
and the
Range
is D1.
Click OK. checks
tion
follows
an
exponential
distribution
withthe
a mean
ofearned
2.5 years.
proportion
of the
tips per 8-hour shift.
population:
45.6
47.7
47.6
46.3

46.2
47.4
49.2
55.8
47.5
48.5
a. Will visit an emergency room within the next 6 months?
b. Will not visit the ER over
theofnext
6 years?
Amount
a. Can Mr. Rutter conclude
that Tip
the mean weight ofNumber
the bags is less than 50 pounds?
c. Will visit an ER next year, but not this year?
Use the .01 significanceuplevel.
to $ 20
d. Find the first and third$0quartiles
of this distribution. 200
b. In a brief report, tell why
Mr.
the z distribution
as the test statistic.
20 upon
to aRutter
50 can use
100 an exponential
73. The times between failures
personal

computer follow
distribution
c. Compute the p-value.50 up to 100
75
with a mean of 300,000 hours. What is the probability of:
27. A new weight-watching100
company,
Weight Reducers International,
advertises that those
up to hours?
200
75
a. A failure in less than 100,000
who join will lose an average
of 10 pounds after the first
two weeks. The standard devior more
b. No failure in the next200
500,000
hours? CHAPTER 12 50
ation is 2.8 pounds. A random sample of
50 The
people
who joined
theof weight
reduction
12–1.
Excel350,000
commands
for
the test

variances on
page 391 are:
c. The next failure occurring
between 200,000
and
hours?
Total
datalevel
for U.S.of
25significance,
in column A and for
I-75we
in colprogram revealed a mean
loss of 9 pounds.a.AtEnter
thethe500
.05
can
d. What are the mean and standard deviation of umn
theB.time
failures?
Labelbetween
the two columns.
conclude that those joining Weight Reducers b.
willSelect
losethe
less
than
10
pounds?
Determine

Data tab on the top menu. Then, on the far right,
theWhat
p-value.
a.
is the probability of a tip of $200 or more?
select Data Analysis. Select F-Test: Two-Sample for
L Y T I 28.
C Sb.
thenof
click
OK.
Dole
Inc. is“$0
concerned
that“$20
the 16-ounce
sliced
pineapple mutually
is being
ArePineapple
the categories
up to $20,”
up toVariances,
$50,”can
and
so
on considered
c. The range of the first variable is A1:A8, and B1:B9 for the
overfilled.
Assume

the standard
deviation
of thesecond.
process
is
ounce.
The
quality-conexclusive?
Clickwww.mhhe.com/lind17e.)
on .03
Labels,
enter 0.05
for Alpha,
select D1 for
11–2. The
Minitab
commands
for
the
two-sample
t-test
on
page
368
(The data for these exercises are available at the text website:
thewere
Output
Range, and
click
OK.arithmetic

trolIfdepartment
tookassociated
a randomwith
sample
50 cans
and totaled,
found
that
the
mean
are:
c.
the probabilities
eachofoutcome
what
would
that total
be?
a. Put theweight
amount absorbed
by the Store
brand inAt
C1 the
and the
was
16.05
ounces.
5%
level
of

significance,
can we conclude
thatsold
the
d.
What
is
the
probability
of
a
tip
of
up
to
$50?
74.
Refer
to
the
North
Valley
Real
Estate
data,
which
report
information
on
homes

amount absorbed by the Name brand paper towel in C2.
mean
weight
greater
than
ounces?
Determine
e.
theis
probability
of a16
tip
of
less than
$200? the p-value.
theis
last
year.
b. Fromduring
the What
toolbar,
select
Stat,
Basic Statistics,
and
then
2-Sample,
and click
51.
Winning

allOK.three
Crown”
races of
is the
considered
thecomputed
greatestearlier
feat of
a.
The
mean
selling“Triple
price (in
$ thousands)
homes was
toabepedigree
$357.0,
c. In the next dialog box, select Samples in different colAfter
adeviation
successful
Kentucky
Derby,
Corn on
the Cob to
is estimate
a heavy favorite
at 2
with
standard
ofC2$160.7.

the normal
distribution
the percentumns,racehorse.
select
C1aStore
for the First
column and
Name of Use
the Second,
click
nextselling
to Assume
variances,
to 1
odds
toboxwin
the
Preakness
Stakes.
age
ofthe
homes
forequal
more
than $500.000. Compare this to the actual results. Is price
and click OK.
a. normally
If he is adistributed?
2 to 1 favorite
to win the

Belmont
as well,
what ishow
his probability
of
Try another
test.
If price Stakes
is normally
distributed,
many homes
winning
the aTriple
should
have
price Crown?
greater than the mean? Compare this to the actual number of homes.
b. Construct
What do a
his
chancesdistribution
for the Preakness
Stakes
to be in order for him to be
frequency
of price. What
do have
you observe?
“even
money”

to on
earn
themarket
Triple Crown?
b. The
mean
days
the
is 30 with a standard deviation of 10 days. Use
52. Thethe normal
first card selected
fromtoaestimate
standardthe
52-card
deck
a king.on the market more than
distribution
number
of ishomes
a. 24 days.
If it is returned
to the
is the
probability
that atest.
kingIfwill
beon
drawn
on the
Compare

thisdeck,
to thewhat
actual
results.
Try another
days
the market
second selection?
is normally
distributed, how many homes
should
be
on the market more than the
12–2. The Excel commands for the one-way ANOVA on page 400 are:
b. mean
If the number
king is not
replaced,
whatthis
is the
probability
that
king
will labeled
be
drawn
onWTA,
thePoof days?
Compare
to the

actual
of
homes.
Does
the normal
a. Key innumber
data
intoafour
columns
Northern,
cono, and Branson.
second selection?

Software Commands

Lin66360_ch10_318-352.indd 348

Software examples using Excel, MegaStat®, and Minitab are included throughout the text. The explanations of the
computer input commands are placed at
the end of the text in Appendix C.

Lin66360_ch07_209-249.indd 244

Lin66360_ch05_132-174.indd 168

11–3. The Excel commands for the paired t-test on page 373 are:
a. Enter the data into columns B and C (or any other two columns) in the spreadsheet, with the variable names in the
first row.
b. Select the Data tab on the top menu. Then, on the far right,
select Data Analysis. Select t-Test: Paired Two Sample for

Means, and then click OK.
c. In the dialog box, indicate that the range of Variable 1 is
from B1 to B11 and Variable 2 from C1 to C11, the
Hypothesized Mean Difference is 0, click Labels, Alpha is
.05, and the Output Range is E1. Click OK.

1/14/17 7:02 AM

1/16/17 9:53 PM

b. Select the Data tab on the top menu. Then, on the far right,
select Data Analysis. Select ANOVA: Single Factor, then
click OK.
c. In the subsequent dialog box, make the input range A1:D8,
click on Grouped by Columns, click on Labels in first row,
the Alpha text box is 0.05, and finally select Output Range
as F1 and click OK.

1/14/17 8:29 AM
1/10/17 7:41 PM

780

Lin66360_appc_774-784.indd 780

1/20/17 10:28 AM


126

A REVIEW OF CHAPTERS 1–4

D A T A A N A LY T I C S
44.

Refer to the North Valley real estate data recorded on homes sold during the last
year. Prepare a report on the selling prices of the homes based on the answers to the
following questions. 
a. Compute the minimum, maximum, median, and the first and the third quartiles of
price. Create a box plot. Comment on the distribution of home prices.  
b. Develop a scatter diagram with price on the vertical axis and the size of the home on
the horizontal. Is there a relationship between these variables? Is the relationship
16–7 direct
a. or indirect?
c. For homes without a pool, develop a scatter
Rank diagram with price on the vertical axis
and the size of the home on the horizontal. Do the same for homes with a pool. How
2
do the relationships
between
price
x
y
x and size fory homes without
d a pool anddhomes
with a pool compare?  
Refer 805
to the Baseball
that report information
on

League
45.
232016 data5.5
1
4.5the 30 Major
20.25
Baseball teams for the 2016 season.
777
62 opened, 3.0
9 of operation
−6.0for that stadium.
36.00 For
a. In the data
set, the year
is the first year
each team, use this variable to create a new variable, stadium age, by subtracting
820
60
8.5
8
0.5
0.25
the value of the variable, year opened, from the current year. Develop a box plot
with the 682
new variable,40
age. Are there
which of the stadiums
1.0 any outliers?
4 If so, −3.0
9.00 are

outliers? 
777
70 create3.0
−7.0
49.00 the
b. Using the
variable, salary,
a box plot. 10
Are there any
outliers? Compute
quartiles810
using formula
of your
28(4–1). Write
7.0a brief summary
2
5.0analysis. 25.00
c. Draw a scatter diagram with the variable, wins, on the vertical axis and salary on the
805
30 your conclusions? 
5.5
3
2.5
6.25
horizontal
axis. What are
d. Using the variable, wins, draw a dot plot. What can you conclude from this plot? 
840
42
10.0

5
5.0
25.00
Refer to the Lincolnville School District bus data.
46.
a. Referring777
to the maintenance
cost3.0
variable, develop
plot. What are16.00
the mini55
7 a box
−4.0
mum, first quartile, median, third quartile, and maximum values? Are there any
51
8.5
6
2.5
6.25
outliers?820
b. Using the median maintenance cost, develop a contingency0table with bus
manufac193.00
turer as one variable and whether the maintenance cost was above or below the
median as the other variable. What are your conclusions?

Answers to Self-Review
The worked-out solutions to the Self-Reviews are provided at the end of the text in Appendix E.

17

17

6(193)
= −.170
10(99)
b. H0: ρ = 0; H1: ρ ≠ 0. Reject H0 if t < −2.306 or t > 2.306.
rs = 1 −

BY S E C TI O N

t = −.170√

Section Reviews

A REVIEW OF

China
A REVIEW OF CHAPTERS
1–4Produced 832.8% more steel than the US

130

CASES

The review also includes continuing
cases and several small cases that let
students make decisions using tools
and techniques from a variety of
chapters.

Practice Test
The Practice Test is intended to
give students an idea of content
that might appear on a test and
how the test might be structured.
The Practice Test includes both
objective questions and problems
covering the material studied in
the section.


*

CHAPTER 17

*

This section is a review of the
and terms
introduced in Chapters 1–4. Chapter 1 began by describing the
5.major
Referconcepts
to the following
diagram.
meaning and purpose of statistics. Next we described the different types of variables and the four levels of measurement.
Chapter 2 was concerned with describing a set of observations by organizing it into a frequency distribution and then
portraying the frequency distribution as a histogram or a frequency polygon. Chapter 3 began by describing measures of
location, such as the mean, 17–1
weighted mean,
1. median, geometric mean, and mode. This chapter also included measures of

dispersion, or spread. Discussed in this section
were the range,Amount
variance, and Index
standard
deviation. Chapter 4 included
Country
(Based=US)
several graphing techniques such as dot plots, box plots, and scatter diagrams. We also discussed the coefficient of skew0
40
80
160
ness, which reports the lack of symmetry in a China
set of data.
822.7120
932.8 200
Throughout this section we stressed the importance of statistical software, such as Excel and Minitab. Many computer
110.7
125.5
a. What ishow
theJapan
graph called?
outputs in these chapters demonstrated
quickly
and effectively a large data set can be organized into a frequency
b. What
the
median,
and first
third quartile
values?

United
States
88.2
100.0
distribution, several of the measures
of are
location
or measures
of and
variation
calculated,
and
the information presented in
c. Is the distribution positively skewed? Tell how you know.
graphical form.
India
86.5
98.1
d. Are there any outliers? If yes, estimate these values.
Russia the number of71.5
81.1
e. Can you determine
observations in the study?

After selected groups of chapters
(1–4, 5–7, 8 and 9, 10–12, 13 and
14, 15 and 16, and 17 and 18), a
Section Review is included. Much
like a review before an exam, these
include a brief overview of the chapters and problems for review.

Cases

10 − 2
= −0.488
1 − (−0.170) 2
A REVIEW OF CHAPTERS 1–4
129
H01–4
is not rejected. We have not shown a relationship
CHAPTERS
between the two tests.

17

17

2. a.

location,
charts
or draw graphs
2. There
Determine
thesales
mean
and median ofwho
the checking
markets.
are 40

representatives
call di- acA. create
Century
National
Banksuch as a cumulative frequency
distribution,
determine
the quartiles
count
balances.
Compare
theIndex
and thedemedian
on large-volume
customers,
such
asmean
the athletic
The following
case willand
appear
inYear
subsequent
reviewAverage
sec- rectlyHourly
Earnings
(1995
=
Base)
for both

men
and
women.
Develop
the
charts
and
write
balances
for
the
four
branches.
Is
there
a difference
partments
at
major
colleges
and
universities
and
tions. Assume that you work in the Planning Department of
the report
summarizing
theBank
yearly
employees
among

thefranchises.
branches? There
Be sureare
to 30
explain
the
difference
sports
sales
reprethe Century
National
andsalaries
report
Lamberg. You professional
1995toof Ms.
11.65
100.0
at Wildcat
Plumbing
Supply.
Does it appear that there are
the mean
the median
in your
report.
sentatives between
who represent
theand
company
to retail

stores
lowill need
to do some
Lin66360_ch04_094-131.indd
126data analysis and prepare a short writ2000
14.02
120.3
pay differences
on gender?
3. shopping
Determine
the and
range
and discounters
the standard
deviation
of
malls
large
such
as
ten report. based
Remember,
Mr. Selig is the president of the bank, cated in
the
checking
account
balances.
What
do

the
and Target.
2005
138.5first and
so you will
want to ensure
that your
report is complete and Kmart 16.13
C. Kimble
Products:
Is There
a Difference
quartiles
show? Determine
the the
coefficient
of
Upon third
his return
to corporate
headquarters,
CEO
accurate. A copy of the data appears
in
Appendix
A.6.
2013
19.97
171.4
In the

Commissions?
andfor
indicate
it shows.
Because
sales manager
a reportwhat
comparing
the comCentury National Bank has offices in several cities in asked the skewness
At thethe
January
national
sales
meeting,
the
CEO
of
Kimble
Mr. Selig
does
not
deal
with
statistics
daily,
include
a
2016
21.37
183.4

missions
earned
last
year
by
the
two
parts
of
the
sales
Midwest and the southeastern part of the United
Products
wasMr.
questioned
extensively
com-like to team. The brief
description
and interpretation
the standard
information
is reported
below. Write of
a brief
reStates.
Dan Selig,
presidentregarding
and CEO,the
would
pany policy

for
paying
commissions
to
its
sales
represen2016
Average
wage
Increased
83.4%
from
1995
deviation
and
other
measures.
port.
Would
you
conclude
that
there
is
a
difference?
Be
know the characteristics of his checking account customtatives.
The
company

sells sporting
goods
to two major
sure to include information in the report on both the ceners.
What
is the balance
of a typical
customer?
B. Wildcat Plumbing Supply Inc.:
How many other bank services do the checking
ac- tral tendency and dispersion of the two groups.
b.
Do We Have Gender Differences?
Commissions
Earned by Sales
count customers
use?Representatives
Do the customers use the ATM serWildcat Earned
Plumbing
Supply
has served the plumbing
Calling
on and,
Athletic
Departments
($) WhatYear
Commissions
by Sales
Representatives
vice

if so,
how often?
about debit cards?
Who Hourly
Average
Earnings
Index
(1995 – 2000 = Base)
needs
of Southwest
Calling
on
Large
Retailers ($) Arizona for more than 40 years.
them,
how69often
they used?
354uses87
1,676and
1,187
3,202are 680
39 1,683 1,106
The company was founded by Mr. Terrence
St. Julian
1995
To better
understand
customers,
Mr. Selig
883 3,140

299 2,197
175 159the1,105
434 615
149 asked 11.65
1,116 681 1,294
12 754 1,206 1,448 870 90.8
944 1,255
and is run today by his son Cory. The company has
Wendy
of 2000
planning,
1,168Ms.278
579Lamberg,
7 357director
252 1,602
2,321 to 4select
392 a sam- 14.02
1,213 1,291 719 934 1,313 1,083 899 850109.2
886 1,556
grown from a handful of employees to more than 500
ple
of
customers
and
prepare
a
report.
To
begin,
she

has
416 427 1,738 526 13 1,604 249 557 635 527
886 1,315 1,858 1,262 1,338 1,066 807 1,244 758 918
today. Cory is concerned about several
positions within
125.7
appointed a team from her staff.2005
You are the head of the 16.13
the company where he has men and women doing esteam and responsible for preparing
the report. You select a 19.97
2013
155.6
sentially
the
same
job
but
at
different
pay. To investirandom sample of 60 customers. In addition to the balance
gate, he collected the information below.
Suppose you
2016
166.5
PRA
C T Iaccount
CE T
T of last
in each
at E

theSend
month, you determine 21.37
are a student intern in the Accounting Department and
(1) the number of ATM (automatic teller machine) transac2016
Average
wage
Increased
86.5%
from
the
average
of
1995,
2000
have
been
given
the
task
to
write
a
report
summarizing
Theretions
is a practice
test
at
the
end

of
each
review
section.
The
tests
are
in
two
parts.
The
first
part
contains
several
objecin the last month; (2) the number of other bank serthe
situation.In most cases, it should take 30 to 45
tive questions,
usuallyaccount,
in a fill-in-the-blank
Theetc.)
second
problems.
vices (a savings
a certificate format.
of deposit,
the part is
minutes
to complete
test. Thethe

problems
require
calculator.
Check the answers in the Answer Section in the back of
customer
uses; the
(3) whether
customer
aadebit
card
17–2
1.hasa.
P1 =
($85/$75)(100)
113.3
Yearly =
Salary
($000)
Women
Men
the book.
(this is a bank service in which charges are made directly
to
Less than
2
0
= 30
112.5
P2 = ($45/$40)(100)
the customer’s account); and (4) whether or not interest

is
Part paid
1—Objective
30 up to 40
3
1
on the checking account. The sample includes cusPAtlanta,
= (113.3
+ 112.5)/2
tomers
fromof the
branches
in Cincinnati,
Ohio;analyzing,
1. The
science
collecting,
organizing,
presenting,
and interpreting
to 112.9
assist in
40 data
up to=
50
17
4
Georgia;
Louisville,
Kentucky;

and Erie, Pennsylvania.
.
making
effective
decisions
is called
50 up to =
60 113.0 1. 17
24
b.
P = ($130/$115)(100)
2. Methods of organizing, summarizing, and presenting data in an informative60
way
are
up to 70
8
21
1. Develop a graph or table that portrays the checking
$85(500) + $45(1,200)
.
called
70 up to 80
7
balances. What is the balance of a typical
c.customer?
P=
(100)2. 3
3. The entire set of individuals or objects of interest or the measurements obtained
from all
80 or more

0
3
$75(500)
+
$40(1,200)
Do many customers have more than $2,000 in their
individuals or objects of interest are called the
.
3.
accounts? Does it appear that there is a difference in
4. List the two types of variables.
$96,500
To kick off the project, Mr. Cory4.St. Julian held a meeting
the distribution of the accounts among the four
(100)
=and112.9
5. The number of bedrooms in a house is an example of=a
. (discrete
variable,
with
his staff
you were invited. At this meeting, it was
branches? Around what value do the account bal85,500
continuous variable, qualitative variable—pick one)
5. several measures of
suggested that you calculate
ances tend to cluster?
6. The jersey numbers of Major League Baseball players are an example of what level of
measurement?
6.

7. The classification of students by eye color is an example of what level of measurement?
7.
8. The sum of the differences between each value and the mean is always equal to what value? 8.
9. A set of data contained 70 observations. How many classes would the 2k method suggest to
construct a frequency distribution?
9.
10. What percent of the values in a data set are always larger than the median?
10.
11. The square of the standard deviation is the
.
11.

xi

17

1/10/17 7:4

C

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• Test Bank  The Test Bank, revised by Wendy Bailey of Troy University, contains hundreds of true/false, multiple
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Once installed, MegaStat will always be available on the Excel add-ins ribbon with no expiration date or data limitations. MegaStat performs statistical analyses within an Excel workbook. When a MegaStat menu item is selected, a
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xiv


AC KN OWLE DG M E NTS

This edition of Statistical Techniques in Business and Economics is the product of many people: students, colleagues, reviewers, and

the staff at McGraw-Hill Education. We thank them all. We wish to express our sincere gratitude to the reviewers:

Stefan Ruediger
Arizona State University
Anthony Clark
St. Louis Community College
Umair Khalil
West Virginia University
Leonie Stone
SUNY Geneseo

Golnaz Taghvatalab
Central Michigan University
John Yarber
Northeast Mississippi Community
College
John Beyers
University of Maryland

Mohammad Kazemi
University of North Carolina
Charlotte
Anna Terzyan
Loyola Marymount University
Lee O. Cannell
El Paso Community College

Their suggestions and thorough reviews of the previous edition and the manuscript of this edition make this a better text.
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­Arcaro, Lakeland Community College, accuracy checked the Connect exercises. Ed Pappanastos, Troy

University, built new data sets and revised Smartbook. Rene Ordonez, Southern Oregon University,
built the Connect guided examples. Wendy Bailey, Tory University, prepared the test bank. Stephanie
Campbell, Mineral Area College, prepared the Powerpoint decks. Vickie Fry, Westmoreland County
Community College, provided countless hours of digital accuracy checking and support.
We also wish to thank the staff at McGraw-Hill. This includes Dolly Womack, Senior Brand Manager; Michele Janicek, Product Developer Coordinator; Camille Corum and Ryan McAndrews, Product
Developers; Harvey Yep and Bruce Gin, Content Project Managers; and others we do not know personally, but who have made valuable contributions.



xv


xvi
CONTENTS
EN
H A N C E M E NTS TO

STATI STI CA L TE C H N I QU E S
I N BUS I N E SS & E CO N O M I C S , 17E

MAJOR CHANGES MADE TO INDIVIDUAL
CHAPTERS:
CHAPTER 1  What Is Statistics?

CHAPTER 8  Sampling Methods and the Central
Limit Theorem
• New Data Analytics section with new data and questions.

• Revised Self-Review 1-2.

CHAPTER 9  Estimation and Confidence Intervals

• New Section describing Business Analytics and its integration
with the text.

• New Self-Review 9-3 problem description.

• Updated exercises 2, 3, 17, and 19.

• New Data Analytics section with new data and questions.

• New Data Analytics section with new data and questions. 

CHAPTER 2  Describing Data: Frequency Tables,
Frequency Distributions, and Graphic Presentation

• Updated exercises 5, 6, 12, 14, 23, 24, 33, 41, 43, and 61.

CHAPTER 10  One-Sample Tests
of Hypothesis

• Revised chapter introduction.

• Revised the Example/Solutions using an airport, cell phone
parking lot as the context.

• Added more explanation about cumulative relative frequency
distributions.

• Revised the section on Type II error to include an additional

example.

• Updated exercises 47 and 48 using real data.

• New Type II error exercises, 23 and 24.

• New Data Analytics section with new data and questions.

• Updated exercises 19, 31, 32, and 43.

CHAPTER 3  Describing Data:
Numerical Measures
• Updated Self-Review 3-2.
• Updated Exercises 16, 18, 73, 77, and 82.
• New Data Analytics section with new data and questions.

CHAPTER 4  Describing Data: Displaying and
Exploring Data

• New Data Analytics section with new data and questions.

CHAPTER 11  Two-Sample Tests
of Hypothesis
• Updated exercises 5, 9, 12, 26, 27, 30, 32, 34, 40, 42,
and 46.
• New Data Analytics section with new data and questions.

CHAPTER 12  Analysis of Variance

• Updated exercise 22 with 2016 New York Yankee player

salaries.

• Revised Self-Reviews 12-1 and 12-3.

• New Data Analytics section with new data and questions.

• New Data Analytics section with new data and questions.

CHAPTER 5  A Survey of Probability Concepts

CHAPTER 13  Correlation and Linear Regression

• Revised the Example/Solution in the section on Bayes
Theorem.

• Added new conceptual formula, to relate the standard error
to the regression ANOVA table.

• Updated exercises 45 and 58 using real data.

• Updated exercises 36, 41, 42, 43, and 57.

• New Data Analytics section with new data and questions.

• New Data Analytics section with new data and questions.

CHAPTER 6  Discrete Probability Distributions

CHAPTER 14  Multiple Regression Analysis

• Expanded discussion of random variables.

• Updated exercises 19, 21, 23, 24, and 25.

• Revised the Example/Solution in the section on Poisson
distribution.

• New Data Analytics section with new data and questions.

• Updated exercises 18, 58, and 68.
• New Data Analytics section with new data and questions.

CHAPTER 7  Continuous Probability Distributions

• Updated exercises 10, 21, 24, 33, 38, 42, and 44.

CHAPTER 15  Nonparametric Methods: Nominal
Level Hypothesis Tests
• Updated the context of Manelli Perfume Company Example/
Solution.

• Revised the Example/Solutions using Uber as the context.

• Revised the “Hypothesis Test of Unequal Expected Frequencies” Example/Solution.

• Updated exercises 19, 22, 28, 36, 47, and 64.

• Updated exercises 3, 31, 42, 46, and 61.

• New Data Analytics section with new data and questions.

• New Data Analytics section with new data and questions.

• Revised Self-Review 7-1.

xvi


CHAPTER 16  Nonparametric Methods: Analysis of
Ordinal Data

CHAPTER 18  Time Series and Forecasting

• Revised the “Sign Test” Example/Solution.

• New Data Analytics section with new data and questions.

• Revised the “Testing a Hypothesis About a Median” Example/
Solution.
• Revised the “Wilcoxon Rank-Sum Test for Independent Populations” Example/Solution.
• Revised Self-Reviews 16-3 and 16-6.
• Updated exercise 25.

• Updated dates, illustrations, and examples.

CHAPTER 19  Statistical Process Control and
Quality Management
• Updated 2016 Malcolm Baldridge National Quality Award
winners.
• Updated exercises 13, 22, and 25.

• New Data Analytics section with new data and questions.

CHAPTER 17  Index Numbers
• Revised Self-Reviews 17-1, 17-2, 17-3, 17-4, 17-5, 17-6, 17-7.
• Updated dates, illustrations, and examples.
• New Data Analytics section with new data and questions.



xvii



BRIEF CONTENTS

1 What is Statistics?  1
2 Describing Data: Frequency Tables, Frequency Distributions,
and Graphic Presentation 

18

3 Describing Data: Numerical Measures  51
4 Describing Data: Displaying and Exploring Data  94
5 A Survey of Probability Concepts  132
6 Discrete Probability Distributions  175
7 Continuous Probability Distributions  209
8 Sampling Methods and the Central Limit Theorem 
9 Estimation and Confidence Intervals  282
10 One-Sample Tests of Hypothesis  318

11 Two-Sample Tests of Hypothesis  353
12 Analysis of Variance  386
13 Correlation and Linear Regression  436
14 Multiple Regression Analysis  488
15 Nonparametric Methods:
Nominal Level Hypothesis Tests 

16
7
1
18
19
20

Nonparametric Methods:
Analysis of Ordinal Data 
Index Numbers 

Review Section

250
Review Section

Review Section

Review Section

545

582

Review Section

621

Time Series and Forecasting 

653

Review Section

Statistical Process Control and Quality Management 
An Introduction to Decision Theory 

Glossary 
Index 

697

728

Appendixes:
Data Sets, Tables, Software Commands, Answers 



Review Section

745

847

851

xix


CONTENTS

A Note from the Authors 

vi

1What is Statistics? 

1

Introduction  2
Why Study Statistics?  2
What is Meant by Statistics?  3

E X E RC ISE S  41
Chapter Summary  42
Chapter Exercises  43
Data Analytics  49

Types of Statistics  4
Descriptive Statistics  4
Inferential Statistics  5
Types of Variables  6

Levels of Measurement  7
Nominal-Level Data  7
Ordinal-Level Data  8
Interval-Level Data  9
Ratio-Level Data  10
EX ERCISES   11
Ethics and Statistics  12
Basic Business Analytics  12

3Describing Data:

NUMERICAL MEASURES 
51
Introduction  52
Measures of Location  52
The Population Mean  53
The Sample Mean  54
Properties of the Arithmetic
Mean 55
E X E RC ISE S  56

Chapter Summary  13

The Median  57
The Mode  59

Chapter Exercises  14

E X E RC ISE S  61

Data Analytics  17

The Relative Positions of the Mean,
Median, and Mode  62
E X E RC ISE S  63

2Describing Data:


FREQUENCY TABLES, FREQUENCY
DISTRIBUTIONS, AND GRAPHIC
PRESENTATION  18

E X E RC ISE S  66
The Geometric Mean  66

Introduction  19

E X E RC ISE S  68

Constructing Frequency Tables  19

Why Study Dispersion?  69

Relative Class Frequencies  20
Graphic Presentation
of Qualitative Data  21
EX ERCISES   25
Constructing Frequency
Distributions  26

Relative Frequency Distribution  30
EX ERCISES   31
Graphic Presentation of a Distribution  32
Histogram 32
Frequency Polygon  35
EX ERCISES   37
Cumulative Distributions  38

xx

Software Solution  64
The Weighted Mean  65

Range 70
Variance 71
E X E RC ISE S  73
Population Variance  74
Population Standard Deviation  76
E X E RC ISE S  76
Sample Variance and Standard
Deviation 77
Software Solution  78
E X E RC ISE S  79
Interpretation and Uses of the Standard
Deviation 79
Chebyshev’s Theorem  79
The Empirical Rule  80


xxi

CONTENTS

EXER C ISES   81

E X E RC ISE S  140

The Mean and Standard Deviation
of Grouped Data  82

Rules of Addition for Computing
Probabilities  141

Arithmetic Mean of Grouped Data  82
Standard Deviation of Grouped Data  83
EXER C ISES   85
Ethics and Reporting Results  86

E X E RC ISE S  146

Chapter Summary  86

Rules of Multiplication
to Calculate Probability  147

Pronunciation Key  88
Chapter Exercises  88
Data Analytics  92

Special Rule of Multiplication  147

General Rule of Multiplication  148
Contingency Tables  150

4Describing Data:


Special Rule of Addition  141
Complement Rule  143
The General Rule of Addition  144

DISPLAYING AND EXPLORING DATA  94

Tree Diagrams  153
E X E RC ISE S  155
Bayes’ Theorem  157

Introduction  95

E X E RC ISE S  161

Dot Plots  95

Principles of Counting  161

Stem-and-Leaf Displays  96
EXER C ISES   101
Measures of Position  103
Quartiles, Deciles, and Percentiles  103
EXER C ISES   106

The Multiplication Formula  161
The Permutation Formula  163
The Combination Formula  164
E X E RC ISE S  166
Chapter Summary  167
Pronunciation Key  168

Box Plots  107

Chapter Exercises  168

EXER C ISES   109

Data Analytics  173

Skewness  110
EXER C ISES   113
Describing the Relationship between
Two Variables  114
Contingency Tables  116

6Discrete Probability
Distributions  175
Introduction  176

EXER C ISES   118
Chapter Summary  119

What is a Probability Distribution?  176

Pronunciation Key  120

Random Variables  178

Chapter Exercises  120
Data Analytics  126

Discrete Random Variable  179
Continuous Random Variable  179
The Mean, Variance, and Standard Deviation of a
Discrete Probability Distribution  180

Problems  127
Cases  129

Mean 180
Variance and Standard Deviation  180

Practice Test  130

E X E RC ISE S   182

5A Survey of Probability
Concepts  132
Introduction  133
What is a Probability?  134
Approaches to Assigning Probabilities  136
Classical Probability  136
Empirical Probability  137
Subjective Probability  139

Binomial Probability Distribution  184
How Is a Binomial Probability
Computed? 185
Binomial Probability Tables  187
E X E RC ISE S   190
Cumulative Binomial Probability
Distributions 191
E X E RC ISE S   193
Hypergeometric Probability Distribution  193


xxiiCONTENTS
EX ERCISES   197

E X E RC ISE S  257

Poisson Probability Distribution  197

Sampling “Error”  259

EX ERCISES   202

Sampling Distribution of the Sample Mean  261

Chapter Summary  202

E X E RC ISE S  264

Chapter Exercises  203

The Central Limit Theorem  265

Data Analytics  208

7Continuous Probability
Distributions  209
Introduction  210
The Family of Uniform Probability
Distributions  210
EX ERCISES   213
The Family of Normal Probability Distributions  214
The Standard Normal Probability
Distribution  217
Applications of the Standard Normal
Distribution 218
The Empirical Rule  218
EX ERCISES   220
Finding Areas under the Normal Curve  221
EX ERCISES   224
EX ERCISES   226
EX ERCISES   229
The Normal Approximation
to the Binomial  229
Continuity Correction Factor  230
How to Apply the Correction Factor  232
EX ERCISES   233
The Family of Exponential Distributions  234

EX ERCISES   238
Chapter Summary  239
Chapter Exercises  240
Data Analytics  244
Problems  246
Cases  247
Practice Test  248

8Sampling Methods and the
Central Limit Theorem  250
Introduction  251
Sampling Methods  251
Reasons to Sample  251
Simple Random Sampling  252
Systematic Random Sampling  255
Stratified Random Sampling  255
Cluster Sampling  256



E X E RC ISE S  271
Using the Sampling Distribution of the
Sample Mean  273
E X E RC ISE S  275
Chapter Summary  275
Pronunciation Key  276
Chapter Exercises  276
Data Analytics  281

9Estimation and Confidence

Intervals  282
Introduction  283
Point Estimate for a Population Mean  283
Confidence Intervals for a Population Mean  284
Population Standard Deviation, Known σ  284
A Computer Simulation  289
E X E RC ISE S   291
Population Standard Deviation, σ Unknown  292
E X E RC ISE S   299
A Confidence Interval for a Population
Proportion  300
E X E RC ISE S   303
Choosing an Appropriate Sample Size  303
Sample Size to Estimate a Population Mean  304
Sample Size to Estimate a Population
Proportion 305
E X E RC ISE S   307
Finite-Population Correction Factor  307
E X E RC ISE S   309
Chapter Summary  310
Chapter Exercises  311
Data Analytics  315
Problems  316
Cases  317
Practice Test  317

10One-Sample Tests
of Hypothesis  318
Introduction  319
What is Hypothesis Testing?  319

xxiii

CONTENTS

Six-Step Procedure for Testing
a Hypothesis  320
Step 1: State the Null Hypothesis (H0) and the
Alternate Hypothesis (H1) 320
Step 2: Select a Level of Significance  321
Step 3: Select the Test Statistic  323
Step 4: Formulate the Decision Rule  323
Step 5: Make a Decision  324
Step 6: Interpret the Result  324
One-Tailed and Two-Tailed Hypothesis Tests  325
Hypothesis Testing for a Population Mean: Known
Population Standard Deviation  327
A Two-Tailed Test  327
A One-Tailed Test  330
p-Value in Hypothesis Testing  331

Chapter Exercises  378
Data Analytics  385

12Analysis of Variance 
Introduction  387

Comparing Two Population Variances  387
The F Distribution  387

Testing a Hypothesis of Equal Population
Variances 388
E X E RC ISE S  391
ANOVA: Analysis of Variance  392
ANOVA Assumptions  392
The ANOVA Test  394

EXER C ISES   333

E X E RC ISE S  401

Hypothesis Testing for a Population Mean:
Population Standard Deviation Unknown  334

Inferences about Pairs of Treatment
Means  402

EXERC ISES   339

E X E RC ISE S  404

A Statistical Software Solution  340

Two-Way Analysis of Variance  406

EXERC ISES   342

E X E RC ISE S  411

Type II Error  343

Two-Way ANOVA with Interaction  412

EXERC ISES   346
Chapter Summary  347
Pronunciation Key  348
Chapter Exercises  348
Data Analytics  352

386

Interaction Plots  412
Testing for Interaction  413
Hypothesis Tests for Interaction  415
E X E RC ISE S  417
Chapter Summary  418
Pronunciation Key  420

11Two-Sample Tests
of Hypothesis  353

Chapter Exercises  420
Data Analytics  429
Problems  431

Introduction  354

Cases  433

Two-Sample Tests of Hypothesis: Independent

Samples  354

Practice Test  434

EXERC ISES   359
Comparing Population Means with Unknown
Population Standard Deviations  360
Two-Sample Pooled Test  360
EXERC ISES   364
Unequal Population Standard
Deviations 366
EXERC ISES   369
Two-Sample Tests of Hypothesis:
Dependent Samples  370
Comparing Dependent
and Independent Samples  373
EXERC ISES   375

13Correlation and
Linear Regression 

436

Introduction  437
What is Correlation Analysis?  437
The Correlation Coefficient  440
E X E RC ISE S  445
Testing the Significance of the Correlation
Coefficient 447
E X E RC ISE S  450

Regression Analysis  451
Least Squares Principle  451
Drawing the Regression Line  454

Chapter Summary  377

E X E RC ISE S  457

Pronunciation Key  378

Testing the Significance of the Slope  459




xxivCONTENTS
EX ERCISES   461

Qualitative Independent Variables  512

Evaluating a Regression Equation’s
Ability to Predict  462

Regression Models with Interaction  515
Stepwise Regression  517

The Standard Error of Estimate  462
The Coefficient of Determination  463

E X E RC ISE S  519

Review of Multiple Regression  521

EX ERCISES   464

Chapter Summary  527

Relationships among the Correlation
Coefficient, the Coefficient of
Determination, and the Standard
Error of Estimate  464

Pronunciation Key  528
Chapter Exercises  529
Data Analytics  539

EX ERCISES   466

Problems  541

Interval Estimates of Prediction  467

Cases  542

Assumptions Underlying Linear
Regression 467
Constructing Confidence and Prediction
Intervals 468
EX ERCISES   471
Transforming Data  471

Practice Test  543

15Nonparametric Methods:


NOMINAL LEVEL HYPOTHESIS TESTS  545

EX ERCISES   474

Introduction  546

Chapter Summary  475
Pronunciation Key  477

Test a Hypothesis of a Population
Proportion  546

Chapter Exercises  477

E X E RC ISE S  549

Data Analytics  487

Two-Sample Tests about Proportions  550
E X E RC ISE S  554

14Multiple Regression
Analysis  488

Goodness-of-Fit Tests: Comparing Observed and

Expected Frequency Distributions  555
Hypothesis Test of Equal Expected
Frequencies 555

Introduction  489

E X E RC ISE S  560

Multiple Regression Analysis  489

Hypothesis Test of Unequal Expected
Frequencies 562

EX ERCISES   493
Evaluating a Multiple Regression Equation  495

Limitations of Chi-Square  563

The ANOVA Table  495
Multiple Standard Error of Estimate  496
Coefficient of Multiple Determination  497
Adjusted Coefficient of Determination  498

E X E RC ISE S  565
Testing the Hypothesis That a Distribution is
Normal  566

EX ERCISES   499

E X E RC ISE S  569

Inferences in Multiple Linear
Regression  499

Contingency Table Analysis  570
E X E RC ISE S  573

Global Test: Testing the Multiple
Regression Model  500
Evaluating Individual Regression
Coefficients 502

Chapter Summary  574
Pronunciation Key  575
Chapter Exercises  576

EX ERCISES   505

Data Analytics  581

Evaluating the Assumptions of Multiple
Regression  506
Linear Relationship  507
Variation in Residuals Same for Large
and Small ŷ Values  508
Distribution of Residuals  509
Multicollinearity 509
Independent Observations  511

16Nonparametric Methods:


ANALYSIS OF ORDINAL DATA  582
Introduction  583
The Sign Test  583