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S E V E N T H

E D I T I O N

Elementary
Statistics
A Step by Step Approach

Allan G. Bluman
Professor Emeritus
Community College of Allegheny County


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ELEMENTARY STATISTICS: A STEP BY STEP APPROACH, SEVENTH EDITION

Published by McGraw-Hill, a business unit of The McGraw-Hill Companies, Inc., 1221 Avenue of the Americas,
New York, NY 10020. Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved. Previous
editions © 2007, 2004, 2001, 1998, and 1995. 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 The
McGraw-Hill Companies, Inc., 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 the
United States.
This book is printed on acid-free paper.
1 2 3 4 5 6 7 8 9 0 VNH/VNH 0 9 8
ISBN 978–0–07–353497–8
MHID 0–07–353497–8
ISBN 978–0–07–333121–8 (Annotated Instructor’s Edition)
MHID 0–07–333121–X
Editorial Director: Stewart K. Mattson
Sponsoring Editor: Dawn R. Bercier
Director of Development: Kristine Tibbetts
Developmental Editor: Michelle Driscoll
Marketing Manager: John Osgood
Project Manager: April R. Southwood
Lead Production Supervisor: Sandy Ludovissy
Senior Media Project Manager: Sandra M. Schnee
Designer: Tara McDermott
Cover Designer: Rick D. Noel
(USE) Cover Image: © Atlantide Phototravel/Corbis
Senior Photo Research Coordinator: Lori Hancock
Supplement Producer: Mary Jane Lampe
Compositor: ICC Macmillan Inc.
Typeface: 10.5/12 Times Roman
Printer: Von Hoffmann Press

The credits section for this book begins on page 815 and is considered an extension of the copyright page.
Library of Congress Cataloging-in-Publication Data
Bluman, Allan G.
Elementary statistics : a step by step approach / Allan G. Bluman. — 7th ed.
p. cm.
Includes bibliographical references and index.
ISBN 978–0–07–353497–8 — ISBN 0–07–353497–8 (hard copy : acid-free paper)
1. Statistics—Textbooks. I. Title.
QA276.12.B59 2009
519.5—dc22
2008030803

www.mhhe.com


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Brief Contents
CHAPTE R

1

CHAPTE R


2

CHAPTE R

3

CHAPTE R

4

CHAPTE R

CHAPTE R

CHAPTE R

5
6
7

CHAPTE R

8

CHAPTE R

9

Data Description 103


CHAPTE R

10

Probability and
Counting Rules 181

CHAPTE R

11

Discrete Probability
Distributions 251

CHAPTE R

12

The Normal
Distribution 299

CHAPTE R

13

Confidence Intervals
and Sample
Size 355

CHAPTE R


14

The Nature
of Probability
and Statistics 1

Frequency
Distributions
and Graphs 35

Hypothesis
Testing 399

Testing the Difference
Between Two Means,
Two Proportions, and
Two Variances 471

Correlation and
Regression 533

Other Chi-Square
Tests 589

Analysis of
Variance 627

Nonparametric
Statistics 669


Sampling and
Simulation 717

All examples and exercises in this textbook (unless cited) are hypothetical and are presented to enable students to achieve a basic understanding of the statistical concepts explained. These examples and exercises should not be used in lieu of medical, psychological, or other professional advice. Neither the author nor
the publisher shall be held responsible for any misuse of the information presented in this textbook.

iii


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Brief Contents

APPE N DIX

APPE N DIX

A

Algebra Review 751


B–1Writing the Research

APPE N DIX

D

Data Bank 797

APPE N DIX

E

Glossary 805

APPE N DIX

F

Bibliography 813

APPE N DIX

G

Photo Credits 815

APPE N DIX

H


Report 757

APPE N DIX

APPE N DIX

B–2 Bayes’ Theorem 759
B–3 Alternate Approach to
the Standard Normal
Distribution 763

APPE N DIX

C

Tables 767

Index

I–1

Selected
Answers SA–1


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Contents
Preface ix

CHAPTE R

2–2

The Histogram 51
The Frequency Polygon 53
The Ogive 54
Relative Frequency Graphs 56
Distribution Shapes 59

1

The Nature of Probability
and Statistics 1
Introduction 2

1–1
1–2
1–3

Descriptive and Inferential
Statistics 3
Variables and Types of Data 6
Data Collection and Sampling Techniques 9


Histograms, Frequency Polygons,
and Ogives 51

2–3

Other Types of Graphs 68
Bar Graphs 69
Pareto Charts 70
The Time Series Graph 71
The Pie Graph 73
Misleading Graphs 76
Stem and Leaf Plots 80
Summary 94

Random Sampling 10
Systematic Sampling 11
Stratified Sampling 12
Cluster Sampling 12
Other Sampling Methods 13

1–4
1–5

Observational and Experimental Studies 13
Uses and Misuses of Statistics 16

CHAPTE R

Data Description 103


Suspect Samples 17
Ambiguous Averages 17
Changing the Subject 17

Introduction 104

3–1

Detached Statistics 18
Misleading Graphs 18
Faulty Survey Questions 18

Computers and Calculators 19
Summary 25

3–2
CHAPTE R

2

Frequency Distributions
and Graphs 35
Introduction 36

2–1

Organizing Data 37
Categorical Frequency Distributions 38
Grouped Frequency Distributions 39


Measures of Central
Tendency 105
The Mean 106
The Median 109
The Mode 111
The Midrange 114
The Weighted Mean 115
Distribution Shapes 117

Implied Connections 18

1–6

3

Measures of Variation 123
Range 124
Population Variance and Standard Deviation 125
Sample Variance and Standard Deviation 128
Variance and Standard Deviation
for Grouped Data 129
Coefficient of Variation 132
Range Rule of Thumb 133
Chebyshev’s Theorem 134
The Empirical (Normal) Rule 136
v


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Contents

Measures of Position 142
Standard Scores 142
Percentiles 143
Quartiles and Deciles 149
Outliers 151

3–4

5–3
5–4

The Multinomial Distribution 283
The Poisson Distribution 284
The Hypergeometric Distribution 286
Summary 292

Exploratory Data Analysis 162

The Five-Number Summary and Boxplots 162
Summary 171

CHAPTE R

4

Probability and Counting
Rules 181

CHAPTE R

Introduction 300

6–1

4–2
4–3

The Addition Rules for Probability 199
The Multiplication Rules and Conditional
Probability 211
The Multiplication Rules 211
Conditional Probability 216
Probabilities for “At Least” 218

4–4

4–5


6–2

6–3

CHAPTE R

6–4

CHAPTE R

5–2

7

Confidence Intervals
and Sample Size 355
Introduction 356

7–1

Confidence Intervals for the
Mean When s Is Known
and Sample Size 357
Confidence Intervals 358
Sample Size 363

7–2

Introduction 252


5–1

The Normal Approximation to the Binomial
Distribution 340
Summary 347

5

Discrete Probability
Distributions 251

The Central Limit Theorem 331
Distribution of Sample Means 331
Finite Population Correction
Factor (Optional) 337

Probability and Counting Rules 237
Summary 242

Applications of the Normal
Distribution 316
Finding Data Values Given Specific
Probabilities 319
Determining Normality 322

Counting Rules 224
The Fundamental Counting Rule 224
Factorial Notation 227
Permutations 227
Combinations 229


Normal Distributions 302
The Standard Normal
Distribution 304
Finding Areas Under the Standard Normal
Distribution Curve 305
A Normal Distribution Curve as a Probability
Distribution Curve 307

Sample Spaces and
Probability 183
Basic Concepts 183
Classical Probability 186
Complementary Events 189
Empirical Probability 191
Law of Large Numbers 193
Subjective Probability 194
Probability and Risk Taking 194

6

The Normal Distribution 299

Introduction 182

4–1

The Binomial Distribution 270
Other Types of Distributions (Optional) 283


Probability
Distributions 253
Mean, Variance, Standard Deviation,
and Expectation 259

7–3

Mean 259
Variance and Standard Deviation 262
Expectation 264

7–4

Confidence Intervals for the Mean
When s Is Unknown 370
Confidence Intervals and Sample Size
for Proportions 377
Confidence Intervals 378
Sample Size for Proportions 379

Confidence Intervals for Variances
and Standard Deviations 385
Summary 392


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Contents

CHAPTE R

8

Hypothesis Testing 399
Introduction 400

8–1

8–2
8–3
8–4
8–5
8–6

Steps in Hypothesis
Testing—Traditional
Method 401
z Test for a Mean 413

9

Testing the Difference
Between Two Means, Two
Proportions, and

Two Variances 471

9–5

Standard Error of the Estimate 568
Prediction Interval 570
The Multiple Regression Equation 575

CHAPTE R

9–4

Coefficient of Determination 568

10–4 Multiple Regression (Optional) 573

Summary 462

9–3

Types of Variation for the Regression Model 565

t Test for a Mean 427
z Test for a Proportion 437
x2 Test for a Variance or Standard
Deviation 445
Additional Topics Regarding Hypothesis
Testing 457
Type II Error and the Power of a Test 459


9–2

10–3 Coefficient of Determination and Standard
Error of the Estimate 565

P-Value Method for Hypothesis Testing 418

Confidence Intervals and Hypothesis Testing 457

9–1

Determination of the Regression
Line Equation 552

Testing the Significance of R 577
Adjusted R 2 578
Summary 582
CHAPTE R

11

Other Chi-Square Tests 589
Introduction 590

11–1 Test for Goodness
of Fit 591
Test of Normality
(Optional) 596

11–2 Tests Using Contingency Tables 604

Test for Independence 604

Introduction 472

Test for Homogeneity of Proportions 609

Testing the Difference Between
Two Means: Using the z Test 473
Testing the Difference Between Two
Means of Independent Samples:
Using the t Test 484
Testing the Difference Between Two Means:
Dependent Samples 491
Testing the Difference Between
Proportions 503
Testing the Difference Between
Two Variances 512

Summary 619

Summary 523
Hypothesis-Testing Summary 1 531
CHAPTE R

10

Correlation and
Regression 533
Introduction 534


10–1 Scatter Plots and
Correlation 535
Correlation 539

10–2 Regression 551
Line of Best Fit 551

CHAPTE R

12

Analysis of Variance 627
Introduction 628

12–1 One-Way Analysis of
Variance 629
12–2 The Scheffé Test
and the Tukey Test 640
Scheffé Test 640
Tukey Test 642

12–3 Two-Way Analysis of Variance 645
Summary 659
Hypothesis-Testing Summary 2 667
CHAPTE R

13

Nonparametric
Statistics 669

Introduction 670

13–1 Advantages and
Disadvantages of
Nonparametric Methods 671

vii


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Contents

Advantages 671

APPENDIX

A

Algebra Review 751

APPENDIX


B–1

Writing the Research
Report 757

APPENDIX

B–2

Bayes’ Theorem 759

APPENDIX

B–3

Alternate Approach to
the Standard Normal
Distribution 763

APPENDIX

C

Tables 767

APPENDIX

D


Data Bank 797

APPENDIX

E

Glossary 805

APPENDIX

F

Bibliography 813

APPENDIX

G

Photo Credits 815

APPENDIX

H

Selected Answers SA–1

Disadvantages 671
Ranking 671

13–2 The Sign Test 673

Single-Sample Sign Test 673
Paired-Sample Sign Test 675

13–3
13–4
13–5
13–6

The Wilcoxon Rank Sum Test 681
The Wilcoxon Signed-Rank Test 686
The Kruskal-Wallis Test 691
The Spearman Rank Correlation Coefficient
and the Runs Test 697
Rank Correlation Coefficient 697
The Runs Test 700
Summary 708
Hypothesis-Testing Summary 3 714

CHAPTE R

14

Sampling and
Simulation 717
Introduction 718

14–1 Common Sampling
Techniques 719
Random Sampling 719
Systematic Sampling 723

Stratified Sampling 724
Cluster Sampling 726
Other Types of Sampling Techniques 727

14–2 Surveys and Questionnaire Design 734
14–3 Simulation Techniques and the Monte Carlo
Method 737
The Monte Carlo Method 737
Summary 743

Index

I–1


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Page ix

Preface
Approach

Elementary Statistics: A Step by Step Approach was written as an aid in the beginning
statistics course to students whose mathematical background is limited to basic algebra.
The book follows a nontheoretical approach without formal proofs, explaining concepts
intuitively and supporting them with abundant examples. The applications span a broad

range of topics certain to appeal to the interests of students of diverse backgrounds
and include problems in business, sports, health, architecture, education, entertainment,
political science, psychology, history, criminal justice, the environment, transportation,
physical sciences, demographics, eating habits, and travel and leisure.

About This
Book

While a number of important changes have been made in the seventh edition, the learning system remains untouched and provides students with a useful framework in which
to learn and apply concepts. Some of the retained features include the following:
• Over 1800 exercises are located at the end of major sections within each chapter.
• Hypothesis-Testing Summaries are found at the end of Chapter 9 (z, t, x2, and
F tests for testing means, proportions, and variances), Chapter 12 (correlation,
chi-square, and ANOVA), and Chapter 13 (nonparametric tests) to show students
the different types of hypotheses and the types of tests to use.
• A Data Bank listing various attributes (educational level, cholesterol level, gender,
etc.) for 100 people and several additional data sets using real data are included
and referenced in various exercises and projects throughout the book.
• An updated reference card containing the formulas and the z, t, x2, and PPMC
tables is included with this textbook.
• End-of-chapter Summaries, Important Terms, and Important Formulas give
students a concise summary of the chapter topics and provide a good source for
quiz or test preparation.
• Review Exercises are found at the end of each chapter.
• Special sections called Data Analysis require students to work with a data set to
perform various statistical tests or procedures and then summarize the results. The
data are included in the Data Bank in Appendix D and can be downloaded from
the book’s website at www.mhhe.com/bluman.
• Chapter Quizzes, found at the end of each chapter, include multiple-choice,
true/false, and completion questions along with exercises to test students’

knowledge and comprehension of chapter content.
• The Appendixes provide students with an essential algebra review, an outline for
report writing, Bayes’ theorem, extensive reference tables, a glossary, and answers
to all quiz questions, all odd-numbered exercises, selected even-numbered
exercises, and an alternate method for using the standard normal distribution.
ix


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Preface

• The Applying the Concepts feature is included in all sections and gives students
an opportunity to think about the new concepts and apply them to hypothetical
examples and scenarios similar to those found in newspapers, magazines, and radio
and television news programs.

Changes in
the Seventh
Edition

This edition of Elementary Statistics is updated and improved for students and instructors in the following ways:

• Over 300 new exercises have been added, most using real data, and many
exercises incorporate thought-provoking questions requiring students to interpret
their results.
• Titles have been given to each application example and each exercise problem to
emphasize their real-world relevance.
• Six new Speaking of Statistics topics have been included.
• An explanation of bar graphs has been added to Chapter 2 since bar graphs are one
of the most commonly used graphs in statistics, and they are slightly different from
Pareto charts.
• Over 40 examples have been replaced with new ones, the majority using real data.
• Two graphs have been added to the explanation of the chi-square distribution in
Chapter 7 to help clarify the nature of the distribution and how the distribution is
related to the chi-square table.
• The Excel Technology Step by Step boxes have been updated to reflect Microsoft
Excel 2007.
• The shortcut formula for the standard deviation has been changed. The formula used
now is s ϭ

͙

nΘ͚X2 Ι Ϫ Θ͚XΙ 2
, which is the one used in most other statistics books.
nΘn Ϫ 1Ι

•
•
•
•
•


͙

͚X2 Ϫ [Θ͚XΙ 2͞n]
.
nϪ1
Many reviewers have stated that they like the first formula better than the second one.
The cumulative standard normal distribution is used throughout the book.
The null hypothesis is stated using the equals sign in all cases where appropriate.
When s or s1 and s2 are known, the z tests are used in hypothesis testing. When s
or s1 and s2 are unknown, the t tests are used in hypothesis testing.
The F test for two variances is no longer used before the t test for the difference
between two means when s1 and s2 are unknown.
The Data Projects at the end of each chapter are all new and are specific to the
areas of Business and Finance, Sports and Leisure, Technology, Health and
Wellness, Politics and Economics, and Your Class.
It also avoids the complex fraction used in the other formula s ϭ


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Preface

xi


Acknowledgments
It is important to acknowledge the many people whose contributions have gone into the
Seventh Edition of Elementary Statistics. Very special thanks are due to Jackie Miller of
The Ohio State University for her provision of the Index of Applications, her exhaustive
accuracy check of the page proofs, and her general availability and advice concerning all
matters statistical. The Technology Step by Step sections were provided by Gerry
Moultine of Northwood University (MINITAB), John Thomas of College of Lake
County (Excel), and Michael Keller of St. Johns River Community College (TI-83 Plus
and TI-84 Plus).
I would also like to thank Diane P. Cope for providing the new exercises, Kelly
Jackson for writing the new Data Projects, and Sally Robinson for error checking, adding
technology-accurate answers to the answer appendix, and writing the Solutions Manuals.
Finally, at McGraw-Hill Higher Education, thanks to Dawn Bercier, Sponsoring
Editor; Michelle Driscoll, Developmental Editor; John Osgood, Marketing Manager;
April Southwood, Project Manager; Amber Bettcher, Digital Product Manager; and
Sandra Schnee, Senior Media Project Manager.
Allan G. Bluman
Special thanks for their advice and recommendations for revisions found in the Seventh Edition go to
Stan Adamski, Owens Community College
Olcay Akman, Illinois State University
Patty G. Amick, Greenville Technical College
Raid Amin, University of West Florida
Diana J. Asmus, Greenville Technical College
John J. Avioli, Christopher Newport University
Barb Barnet, University of Wisconsin, Platteville
Sr. Prof. Abraham Biggs, Broward Community
College
Wes Black, Illinois Valley Community College
William L. Blubaugh, University of Northern
Colorado

Donna Brouillette, Georgia Perimeter College
Robert E. Buck, Slippery Rock University
David Busekist, Southeastern Louisiana University
Ferry Butar Butar, Sam Houston State University
Keri Catalfomo, TriCounty Community College
Lee R. Clendenning, Berry College
Sarah Trattler Clifton, Southeastern Louisiana
University
Jeff Edmunds, University of Mary Washington
Billy Edwards, University of Tennessee at
Chattanooga
C. Wayne Ehler, Ann Arundel Community College
Hassan Elsalloukh, University of Arkansas at Little
Rock

Thomas Fitzkee, Francis Marion University
Kevin Fox, Shasta College
Dr. Tom Fox, Cleveland State Community College
Leszek Gawarecki, Kettering University
Dana Goodwin, University of Central Arkansas
C. Richard Gumina, Jr., Colorado State University
Shawn Haghighi, Lindenwood University
Elizabeth Hamman, Cypress College
Dr. Willard J. Hannon, Las Positas College
Robert L. Heiny, University of Northern Colorado
Todd Hendricks, Georgia Perimeter College
Jada P. Hill, Richland College
Dr. James Hodge, Mountain State University
Clarence Johnson, Cuyahoga Community College
Craig Johnson, Brigham Young University—Idaho

Anne M. Jowsey, Niagara County Community
College
Linda Kelly-Penny, Midland College
Jong Sung Kim, Portland State University
Janna Liberant, Rockland Community College SUNY
Jackie MacLaughlin, Central Piedmont Community
College
Rich Marchand, Slippery Rock University
Steve Marsden, Glendale College
Michael McKenna, Louisiana State University


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Preface

Ayrin C. Molefe, University of Central Arkansas
Christina Morian, Lincoln University
Alfred K. Mulzet, Florida Community College at
Jacksonville
Humberto Munoz, Southern University and A&M
College at Baton Rouge

Miroslaw Mystkowski, Gardner-Webb University
Michael A. Nasab, Long Beach City College
Jeanne Osborne, Middlesex County College
Elaine S. Paris, Mercy College
Suzie Pickle, St. Petersburg College
Robert H. Prince, Berry College
Aaron Robertson, Colgate University
Kim Gilbert, University of Georgia
Jason Samuels, BMCC
Salvatore Sciandra, Niagara County Community
College
Lynn Smith, Gloucester County College
Dr. M. Jill Stewart, Radford University
Kagba Suaray, California State University,
Long Beach
Gretchen I. Syhre, Hawkeye Community College

Martha Tapia, Berry College
Sherry Taylor, Piedmont Technical College
William Trunkhill, Waubonsee Community College
Jo Tucker, Tarrant County College–SE
Thomas Tunnell, Illinois Valley Community College
Christina Vertullo, Marist College
Dr. Mahbobeh Vezvaei, Kent State University
Tilaka N. Vijithakumara, Illinois State University
Barbara B. Ward, Belmont University
William D. Warde, Oklahoma State University
Brenda Weak, Las Positas College
Glenn Weber, Christopher Newport University
Daniel C. Weiner, Boston University

Jane-Marie Wright, Suffolk County Community
College
Yibao Xu, Borough of Manhattan Community
College, CUNY
Yi Ye, University of North Florida
Jill S. Yoder, North Central Texas College
Quinhong Zhang, Northern Michigan University
James Zimmer, Chattanooga State

Special thanks for their advice and recommendations for revisions found in the Fifth and Sixth Editions go to
Rosalie Abraham, Florida Community College-North
Anne Albert, University of Findlay
Trania Aquino, Del Mar College
Rona Axelrod, Edison Community College
Mark D. Baker, M.S., Illinois State University
Sivanandan Balakumar, Lincoln University
Naveen K. Bansal, Marquette University
Freda Bennett, Massachusetts College of
Liberal Arts
Matthew Bognar, University of Iowa
Andrea Boito, Pennsylvania State University–Altoona
Dean Burbank, Gulf Coast Community College
Christine Bush, Palm Beach Community College–Palm
Beach Gardens
Carlos Canas, Florida Memorial College
James Condor, Manatee Community
College–Bradenton
Diane Cope, Washington & Jefferson College
Gregory Daubenmire, Las Positas College


Melody E. Eldred, State University College–Oneonta
Abdul Elfessi, University of Wisconsin–LaCrosse
Gholamhosse Gharehgozlo Hamedani, Marquette
University
Joseph Glaz, University of Connecticut
Liliana Gonzalez, University of Rhode Island–
Kingston
Rebekah A. Griffith, McNeese State University
Renu A. Gupta, Louisiana State University–Alexandria
Harold S. Hayford, Pennsylvania State University–
Altoona
Shahryar Heydari, Piedmont College
Helene Humphrey, San Joaquin Delta College
Patricia Humphrey, Georgia Southern University
Charles W. Johnson, Collin County Community
College–Plano
Jeffery C. Jones, County College of Morris
Anand Katiyar, McNeese State University
Brother Donald Kelly, Marist College


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Preface


Dr. Susan Kelly, University of Wisconsin–La Crosse
Michael Kent, Borough of Manhattan Community
College
B. M. Golam Kibria, Florida International
University–Miami
Hyun-Joo Kim, Truman State University
Joseph Kunicki, University of Findlay
Marie Langston, Palm Beach Community College–
Lakeworth
Susan S. Lenker, Central Michigan University
Benny Lo, DeVry University
Chip Mason, Belhaven College
Judith McCrory, Findlay University
Lynnette Meslinsky, Erie Community College
Charles J. Miller, Jr., Camden County College
Carla A. Monticelli, Camden County College
Lindsay Packer, College of Charleston
Irene Palacios, Grossmont College
Samuel Park, Long Island University–Brooklyn
Chester Piascik, Bryant University
Leela Rakesh, Central Michigan University
Fernando Rincón, Piedmont Technical College

xiii

Don R. Robinson, Illinois State University
Kathy Rogotzke, North Iowa Area Community
College–Mason City
Deb Rumsey, The Ohio State University

Carolyn Shealy, Piedmont Technical College
Dr. J. N. Singh, Barry University
George Smeltzer, Pennsylvania State University–
Abington
Jeganathan Sriskandarajah, Madison Area Technical
College
Diana Staats, Dutchess Community College
Richard Stevens, University of Alaska–Fairbanks
Richard Stockbridge, University of Wisconsin–
Milwaukee
Linda Sturges, SUNY Maritime College
Klement Teixeira, Borough of Manhattan Community
College
Diane Van Deusen, Napa Valley College
Cassandra L. Vincent, Plattsburgh State
University
David Wallach, Findlay University
Cheng Wang, Nova Southeastern University


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Page xv


Guided Tour: Features
and Supplements

Each chapter begins with an outline
and a list of learning objectives. The
objectives are repeated at the
beginning of each section to help
students focus on the concepts
presented within that section.

590

er 11
Chapt

Tests
quare
Chi-S
Other

C H A P T E

Outline

After completing this chapter, you should be able to

tics
Statis day
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ges, Jr sed with pe
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: J. Hod
Source . 228–229.
pp
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The Normal
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Objectives

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seeds. approxima cessive tra ver the nex ith the the test, which
w
o
-Hill,
remain inant and re 556 seeds tual results ” chi-square is chapter.

cGraw
rk: M
c
d
ew Yo
of dom d examine pared the a d a “simple the end of th
tics (N
n
Statis
t
m
a
se
a
to
o
u
s
n
c
d
a
io
e
e
e
pe
,h
isit
y, h

troduct
cal In
Finall To do this oday—Rev
Empiri
b, An
ics T
orrect.
Stat La
was c See Statist
hfield,
. Crutc
R
r.
d
te
an
p
ch,
.
cha
ission
D. Kre

R

1

Identify distributions as symmetric or skewed.

2

3

Identify the properties of a normal distribution.

4

Find probabilities for a normally distributed
variable by transforming it into a standard
normal variable.

5

Find specific data values for given
percentages, using the standard normal
distribution.

6

Use the central limit theorem to solve
problems involving sample means for large
samples.

7

Use the normal approximation to compute
probabilities for a binomial variable.

Find the area under the standard normal
distribution, given various z values.


al for
interv andence
r st
a confi variance o
d
n
fi
8 to
single
7 and
out a
mple
hapters othesis ab
“If a sa h the
p
d in C
ch as
as use to test a hy
n
ns, su lected wit e of
w
o
o
n
ti
ti
o
u
c
ti

d
du
trib
be se ependenc
tribu
on an
Intro
ncy dis
color
are dis ard deviati
freque will each test the ind
hi-squ
,
erning
The c nce or stand
to
s conc obile colors n be used
a
st
te
r
a vari viation.
a
m
e
used fo
tion c
f auto
dard d an also be a choice o re distribu
It c

iven
i-squa
rs is g y?” The ch
e
y
u
b
nc
of
freque
same

Introduction
6–1

Normal Distributions

6–2 Applications of the Normal Distribution
6–3 The Central Limit Theorem
6–4 The Normal Approximation to the Binomial
Distribution
Summary

6–1

The outline and learning objectives are
followed by a feature titled Statistics
Today, in which a real-life problem shows
students the relevance of the material in
the chapter. This problem is subsequently

solved near the end of the chapter by
using the statistical techniques presented
in the chapter.

11–2

xv


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38

Chapter
2 Freque
ncy Distrib
utions and

Graphs

Two typ
frequency es of frequen
cy
structing distribution and distributions tha

the grou
these dis
t are mo
ped
tributions
st
are show frequency distri often used are
Categor
the
n now.
bution. Th
ical Freq
e proced categorical
ures for
The categ
uency
conDistrib
or
utions
gories, su ical frequency
distribut
ch as nomi
ion
religious
na
is used fo
l- or ordin
affiliatio
r
alda

lev
ta
n,
that can
el data. Fo
or major
Exampl
be
fie
r
pla
ex
ld
am
ce
of study
e 2–1
would us ple, data such as d in specific cateDistribut
e categor
ion of Bl
ical frequ political affiliatio
ood Type
n,
ency distri
Twentys
butions.
five arm
y inductee
data set
is

s were giv
en a blo
od test to
A
determine
B
their blo
B
O
od type.
AB
O
The
O
B
B
AB
B
B
O
A
A
O
O
O
AB
O
A
AB
Construct

O
B
a frequen
A
cy distri
bution fo
Solutio
r the data.
n
Since the
data are
A, B, O,
ca
and AB. tegorical, discre
These typ
te classe
The pr
s can be
es
used
given ne ocedure for cons will be used as
xt.
the classe . There are four
tructing
a frequen
s for the
blood typ
Step 1
cy distri
es:

bution fo distribution.
Make a tab
r categor
le as show
ical data
n.
is
A
B
Class
Tally
C
Frequenc
A
D
y
Percent
B
O
AB

Over 300 examples with detailed solutions
serve as models to help students solve
problems on their own. Examples are solved
by using a step by step explanation, and
illustrations provide a clear display of results
for students.

Step 2
Tally the

data and
Step 3
place the
Count the
results in
tallies an
column B.
Step 4
d pla

ce the res
Find the
ults in co
percenta
lumn C.
ge of value
s in each
class by
using the
formula
where f ϭ
example frequency of the
, in the cla
cla
ss of typ ss and n ϭ total
e A blood
%ϭ 5
, the perce number of value
s. For
ntage is

25 и 100% ϭ 20%
Percenta
ge
s are
be added
since the not normally pa
rt
Also, the
y
decimal are used in certa of a frequency dis
Step 5
equivale
in
Find the
nt of a pe types of graphs tribution, but the
tot
rcent is ca
y
su
table is sh als for columns
lled a re ch as pie graphs can
C
lative fre
own.
.
(frequenc
quency.
y) and D
(percent).
The comp

leted
%ϭ fи
n 100%

Chapter 8 Hypothesis Testing

422

Using this information, answer these questions.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.

What hypotheses would you use?
2–4
Is the sample considered small or large?
What assumption must be met before the hypothesis test can be conducted?
Which probability distribution would you use?
Would you select a one- or two-tailed test? Why?
What critical value(s) would you use?
Conduct a hypothesis test. Use s ϭ 30.3.
What is your decision?

What is your conclusion?
Write a brief statement summarizing your conclusion.
If you lived in a city whose population was about 50,000, how many automobile thefts
per year would you expect to occur?

See page 468 and page 469 for the answers.

Exercises 8–2
For Exercises 1 through 13, perform each of the
following steps.
a.
b.
c.
d.
e.

State the hypotheses and identify the claim.
Find the critical value(s).
Compute the test value.
Make the decision.
Summarize the results.

Use diagrams to show the critical region (or regions),
and use the traditional method of hypothesis testing
unless otherwise specified.
1. Walking with a Pedometer An increase in walking
has been shown to contribute to a healthier life-style. A
sedentary American takes an average of 5000 steps per
day (and 65% of Americans are overweight). A group of
health-conscious employees of a large health care system

volunteered to wear pedometers for a month to record
their steps. It was found that a random sample of 40
walkers took an average of 5430 steps per day, and the
population standard deviation is 600 steps. At a ϭ 0.05
can it be concluded that they walked more than the
mean number of 5000 steps per day?
Source: www.msn.com/health

2. Credit Card Debt It has been reported that the average
credit card debt for college seniors is $3262. The student
senate at a large university feels that their seniors have a
debt much less than this, so it conducts a study of 50
randomly selected seniors and finds that the average debt
is $2995, and the population standard deviation is $1100.
With a ϭ 0.05, is the student senate correct?
Source: USA TODAY.

3. Revenue of Large Businesses A researcher
estimates that the average revenue of the largest
8–24

xvi

businesses in the United States is greater than $24 billion.
A sample of 50 companies is selected, and the revenues (in
billions of dollars) are shown. At a ϭ 0.05, is there enough
evidence to support the researcher’s claim? s ϭ 28.7.
178

122


91

44

35

61
30
29
41
31
24
25
24
22

56
28
16
38
30
16
25
23
21

46
28
16

36
19
15
18
17
20

20
20
19
15
19
15
14
17
17

32
27
15
25
19
19
15
22
20

Source: New York Times Almanac.

4. Salaries of Ph.D. Students Full-time Ph.D. students

receive an average salary of $12,837 according to the
U.S. Department of Education. The dean of graduate
studies at a large state university feels that Ph.D.
students in his state earn more than this. He surveys
44 randomly selected students and finds their average
salary is $14,445, and the population standard deviation
is $1500. With a ϭ 0.05, is the dean correct?
Source: U.S. Department of Education/Chronicle of Higher Education.

5. Health Care Expenses The mean annual expenditure
per 25- to 34-year-old consumer for health care is $1468.
This includes health insurance, medical services, and
drugs and medical supplies. Students at a large university
took a survey, and it was found that for a sample of
60 students, the mean health care expense was $1520,
and the population standard deviation is $198. Is there
sufficient evidence at a ϭ 0.01 to conclude that their
health care expenditure differs from the national average
of $1468? Is the conclusion different at a ϭ 0.05?
Source: Time Almanac.

Numerous examples and exercises
use real data. The icon shown here
indicates that the data set for the
exercise is available in a variety of file
formats on the text’s website and
Data CD.


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Page xvii

Section
9–3 Tes
ting the
Differenc
e Betwe
en Two

Means: Sm
f. Find
all Depen
the test va
dent Sam
ples
lue.
497
t ϭ D Ϫ mD 16
.7
Ϫ0
sD ր 2 n ϭ
25.4 ր 2 6 ϭ 1.610
Step 4
Make the
de

test value cision. The decis
1.610 is
in the no ion is to not rejec
ncritical
region, as t the null hypothe
sis
shown in
Figure 9– , since the
7.

Figure
9–7
Critical
and Test
Values
for Exam
ple 9–7

Numerous Procedure Tables summarize
processes for students’ quick reference.
All use the step by step method.

–2.015
0

Step 5

1.610 2.0
15


Summari
ze
the mine the results. There
ral change
s a perso is not enough ev
n’s chole
idence to
sterol lev
support
the claim
el.
The steps
that
for this t
test are su
mmarize
d in the
Procedur
e Table.
Proced
ure Tabl
e

Testing th
Step 1
Step 2
Step 3

e Differenc


e Betwee

n Means for
State the
Dependen
hypotheses
t
and identi
Find the
fy the cla
critical va
im.
lue(s).
Compute
the test va
lue.
a. Make
a table, as
shown.

Samples

X1

About 4%
of
America
ns spen
d
at least

one night
in jail ea
ch year.

Section 14–1 Common Sampling Techniques

…

A
X2
D‫؍‬X
1 ؊ X
B
2
D 2 ‫( ؍‬X
1 ؊ X )2
2
͚D ϭ
b. Find
the differ
ences an
2
͚D
d place the
ϭ
DϭX Ϫ
res
ult
s in colum
1

X2
n A.
c. Find
the mean
of the dif
ferences.
D ϭ ͚D
n
d. Squa
re the dif
ferences
and place
D 2 ϭ (X
the result
s in colum
1 Ϫ X )2
2
n B. Comp
lete the tab
le.
…

Unusual Stat

723

Speaking of
Statistics

9–27


Should We Be Afraid of Lightning?
The National Weather Service collects
various types of data about the weather.
For example, each year in the United
States about 400 million lightning strikes
occur. On average, 400 people are struck
by lightning, and 85% of those struck
are men. About 100 of these people die.
The cause of most of these deaths is not
burns, even though temperatures as
high as 54,000°F are reached, but heart
attacks. The lightning strike short-circuits
the body’s autonomic nervous system,
causing the heart to stop beating. In
some instances, the heart will restart on
its own. In other cases, the heart victim will need emergency resuscitation.
The most dangerous places to be during a thunderstorm are open fields, golf courses, under trees, and near water,
such as a lake or swimming pool. It’s best to be inside a building during a thunderstorm although there’s no guarantee
that the building won’t be struck by lightning. Are these statistics descriptive or inferential? Why do you think more men
are struck by lightning than women? Should you be afraid of lightning?

Figure 14–4
Method for Selecting
Three-Digit Numbers

s

79
26

18
19
14
29
01
55
84
62
66
48
94
00
46
77
81
40

41
52
13
82
57
12
27
75
95
62
57
13
31

06
16
49
96
46

71
53
41
02
44
18
92
65
95
21
28
69
73
53
44
85
43
15

93
13
30
69
30

50
67
68
96
37
69
97
19
98
27
95
27
73

60
43
56
34
93
06
93
65
62
82
13
29
75
01
80
62

39
23

35
50
20
27
76
33
31
73
30
62
99
01
76
55
15
93
53
75

04
92
37
77
32
15
97
07

91
19
74
75
33
08
28
25
85
96

67
09
74
34
13
79
55
95
64
44
31
58
18
38
01
39
61
68


96
87
49
24
55
50
29
66
74
08
58
05
05
49
64
63
12
13

04
21
56
93
29
28
21
43
83
64
19

40
53
42
27
74
90
99

79
83
45
16
49
50
64
43
47
34
47
40
04
10
89
54
67
49

10
75
46

77
30
45
27
92
89
50
66
18
51
44
03
82
96
64

86
17
83
00
77
45
29
16
71
11
89
29
41
38

27
85
02
11

The Speaking of Statistics sections
invite students to think about poll
results and other statistics-related
news stories in another connection
between statistics and the real world.

Use one column and part of the next column for three digits, that is, 404.

Systematic Sampling
A systematic sample is a sample obtained by numbering each element in the population
and then selecting every third or fifth or tenth, etc., number from the population to be
included in the sample. This is done after the first number is selected at random.

14–7

xvii


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Page xviii

Figure

2–2
Histogr
am
Example for
2–4

Historical Notes, Unusual Stats, and
Interesting Facts, located in the margins,
make statistics come alive for the reader.

d High Tem
peratures

15

Historical Note

9
Graphs
originate
d
when an
6
cient
astronome
rs drew
3
the
position
of the sta

rs in
the heav
0
ens. Roma
n
surveyo
rs also us
99.5°
ed
104.5°
coordina
109.5°
tes to loc
114.5°
ate
landmark
119.5°
s on the
Temperatu
124.5°
Step 2
ir
x
maps.
re (° F)
129.5°
Represen
134.5°
t
the

frequency
The deve
Step 3
lop
on
me
Us
the
nt
of statis
ing the fre
y axis an
tical gra
d
qu
the
en
ph
cies as the
Figure 2–
class boun
s
can be tra
2.
heights,
daries on
ced to
draw verti
William
the x axis.

As the
Playfair
cal bars
109.5–114 histogram show
for each
(1748–1
s,
class. Se
819), an
clusterin .5, followed by 13 the class with the
e
enginee
g around
r and dra
gr
for 114.5
it.
fter
–119.5. Th eatest number of
who used
data
graphs to
e graph als
present
o has on values (18) is
econom
e
Th
pe
ak with the

ic
e Freque
data pic
torially.
data
ncy Po
Anoth
ly

Exampl
e 2–5

Chapter 8 Hypothesis Testing

Again, remember that nothing is being proved true or false. The statistician is only
stating that there is or is not enough evidence to say that a claim is probably true or false.
As noted previously, the only way to prove something would be to use the entire
population under study, and usually this cannot be done, especially when the population
is large.

P-Value Method for Hypothesis Testing
Statisticians usually test hypotheses at the common a levels of 0.05 or 0.01 and sometimes at 0.10. Recall that the choice of the level depends on the seriousness of the
type I error. Besides listing an a value, many computer statistical packages give a
P-value for hypothesis tests.

gon
represen
t the same

data set

The frequ
is by using
a frequen
points plo ency polygon
cy polyg
represen tted for the frequis a graph that dis
on.
ted by the
encies at
pla
heights
the midp ys the data by
of the po
us
oints of
ints.
the class ing lines that co
es. The
Example
frequenciennect
2–5 show
s are
s the

procedur
e for cons
tructing
Record
a frequen
High Te

cy polyg
mperatu
Using the
on.
re
s
frequency
distributio
n given in
Solutio
Example
n
2–4, cons
truct a fre
Step 1
quency po
Find the
mi
lygon.
the uppe dpoints of each
r and low
cla
er bounda ss. Recall that
99.5 ϩ 10
midpoin
ries and
ts are foun
4.5
dividing
by 2:

d by addin
ϭ 102
104.5 ϩ
2
g
109.5
and so on
ϭ 107
2
. The mi
dpoints are
Class bo
undarie
s
Midpoin
ts
99.5–10
Frequenc
4.5
104.5–1
y
102
09.5
109.5–114
107
2
.5
114.5–119
112
8

.5
119.5–124
117
18
124.5–1 .5
122
29
13
129.5–1 .5
127
34.5
7
132
1
1

The P-value (or probability value) is the probability of getting a sample statistic (such as
the mean) or a more extreme sample statistic in the direction of the alternative hypothesis
when the null hypothesis is true.

In other words, the P-value is the actual area under the standard normal distribution curve
(or other curve, depending on what statistical test is being used) representing the probability of a particular sample statistic or a more extreme sample statistic occurring if the
null hypothesis is true.
For example, suppose that an alternative hypothesis is H1: m Ͼ 50 and the mean of
a sample is X ϭ 52. If the computer printed a P-value of 0.0356 for a statistical test,
then the probability of getting a sample mean of 52 or greater is 0.0356 if the true
population mean is 50 (for the given sample size and standard deviation). The relationship between the P-value and the a value can be explained in this manner. For
P ϭ 0.0356, the null hypothesis would be rejected at a ϭ 0.05 but not at a ϭ 0.01. See
Figure 8–18.
When the hypothesis test is two-tailed, the area in one tail must be doubled. For

a two-tailed test, if a is 0.05 and the area in one tail is 0.0356, the P-value will be
2(0.0356) ϭ 0.0712. That is, the null hypothesis should not be rejected at a ϭ 0.05, since
0.0712 is greater than 0.05. In summary, then, if the P-value is less than a, reject the null
hypothesis. If the P-value is greater than a, do not reject the null hypothesis.
The P-values for the z test can be found by using Table E in Appendix C. First find
the area under the standard normal distribution curve corresponding to the z test value;
then subtract this area from 0.5000 to get the P-value for a right-tailed or a left-tailed test.
To get the P-value for a two-tailed test, double this area after subtracting. This procedure
is shown in step 3 of Examples 8–6 and 8–7.
The P-value method for testing hypotheses differs from the traditional method somewhat. The steps for the P-value method are summarized next.

Critical Thinking sections at the end
of each chapter challenge students to
apply what they have learned to new
situations. The problems presented
are designed to deepen conceptual
understanding and/or to extend
topical coverage.

2–19

Rules and definitions are set off for
easy referencing by the student.
248

Chapter
4

Probabil
ity and Co

unting Ru

les

Critica
l Think
ing Ch
1. Con M
allenge
an
3 coins. Game Consider
s
One co
this pr

oblem: A
in ha
on each
con
side. A se s been specially
cond coin
made an man has
on each
d ha
sid
ha
For ex
and a tai e it has a tail. Fina s been specially s a head
l on it. Al
made, an

room. Th ample, suppose
lly
,
a
third
l co
d
there we
The con
e
re
man place ins are of the sa coin has a head
would be probability that
me deno
each had 3 people in the
s the 3 co
and show
mi
a differe
ins
s
na
nt birthda
you even you one side. It is in his pocket, se tion.
y
365 36
money tha
lects one,
heads. He
4

reasonin
t
36
is
it
willing to
3 365 P
is the tw
{
g is that
{
36
o5
be
he
3
t
ϭ
it
36
ad
ca
head is sh
5 365
ed coin.
3 ϭ 0.992
His
owing; the n’t be the two-tai
36
5

Hence, th
being the
led coin
refore, the
e
sin
pr
two-head
ob
re
ce
abili
is a 50
a
have the
(Hint: Se
ed
same birth ty that at least 2
e Exercise coin. Would yo -50 chance of it
of the 3
day will
u tak
1 in Data
2. de M
people wi
be
Projects.) e the bet?
éré Dice
1 Ϫ 0.992
ll

Gam
when he
ϭ 0.008
bet unsu e Chevalier de
Hence, fo
Méré wo
specting
he could
r
k
n
pa
people, th
ge
money
trons that
bet that in t at least one 6,
e
in
fo
rm
4
rolls
ula is
bu
P(at least
double 6. 24 rolls of 2 dice t he lost money of 1 die,
2 people
wh
, he coul

Using th
have the
d get at lea en he
e probab
probabili
same birth
ili
ty
st a
ϭ 1 Ϫ 365 Pk
day)
majority of each event an ty rules, find the
k
of the tim
d explain
36
5
majority
e on the
why he wo
fir
of
n the
Using yo
(Hint: Fi the time when pl st game but lost
ur
calculator
nd the pr
th
that for at

obabilitie aying the second e
, co
subtract
lea
from 1.)
s of losin
ga
same birth st a 50% chan mplete the table
g each ga me.
ce of 2 pe
an
day, 23 or
3. Classic
me and
ople havi d verify
more pe
al Bi
ng
ople will
think need rthday Problem
be needed the
Probabili
.
same bir to be in a room so How many peop
ty
thday (m
le do you
that 2 pe
Nu
th

m
at
be
on
op
This would
r of
at least
th and da
le will ha
y)
people
2 have th
but how , of course, guara ? You might thi ve the
e
nk
many pe
nte
it
e
same bi
is 366.
it (excludin
ople would
there wo
rthday
g lea
uld
1
ne

born on the be a 90% probab ed to be in a room p year),
2
0.000
ility
so that
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xviii

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328

Chapter
6 The No
rmal

Distribu
tion

41. Box

O
the box of ffice Revenues
The data
a random fice total revenu
shown re
e
ly
pr
2001. Ch selected sample (in millions of do esent
eck for no
lla
of the to
p-grossin rs) for
rmality.
294 241
g films in
130 144
71 67
113 70
67 56
Source:
180 199 97 94 91 20
USA TO
DAY.
2 74 79
165 114
60 56
53 51

Techno

logy St
ep by

M IN ITAB

At the end of appropriate sections,
Technology Step by Step boxes show
students how to use MINITAB, the TI-83
Plus and TI-84 Plus graphing calculators,
and Excel to solve the types of problems
covered in the section. Instructions are
presented in numbered steps, usually in the
context of examples—including examples
from the main part of the section. Numerous
computer or calculator screens are
displayed, showing intermediate steps as
well as the final answer.
572

Step by St
ep

Check
for Out
liers

Interpreting Simple Linear Regression

6–30


1.
2.
3.
4.
5.
6.
7.
8.
9.
10.

Are both variables moving in the same direction?
Which number measures the distances from the prediction line to the actual values?
Which number is the slope of the regression line?
Which number is the y intercept of the regression line?
Which number can be found in a table?
Which number is the allowable risk of making a type I error?
Which number measures the variation explained by the regression?
Which number measures the scatter of points about the regression line?
What is the null hypothesis?
Which number is compared to the critical value to see if the null hypothesis should be
rejected?
11. Should the null hypothesis be rejected?

See page 588 for the answers.

Data Projects

2. Sports and Leisure Use the top home run hitter from
each major league baseball team as the data set. Find

the mean and the standard deviation for the number of
home runs hit by the top hitter on each team. Find a
95% confidence interval for the mean number of home
runs hit.
3. Technology Use the data collected in data project 3 of
Chapter 2 regarding song lengths. Select a specific
genre and compute the percentage of songs in the
sample that are of that genre. Create a 95% confidence
interval for the true percentage. Use the entire music
library and find the population percentage of the library
with that genre. Does the population percentage fall
within the confidence interval?

ining

Normal
There ar
ity
e severa
l ways in
which sta
ct a Hist
tisticians
ogram
test a da
Inspect th
ta set for
e histogr
normality.
am for

shape.
Four are
shown he
re.
1. Enter
the data
in the fir
column
st
of a new
workshee
t. Name
th
column
Inventory. e
2. Use St
at
Statistic >Basic
s
Summar >Graphical
y presen
ted in
Section
3–
the histo 3 to create
gram. Is
it
symmetr
ic?
single pe Is there a

ak?
Constru

5 29
34 44
45
63 68
74 74
81
88 91
97 98
113
118 151
158

Applying the Concepts 10–3

1. Business and Finance Use 30 stocks classified as the
Dow Jones industrials as the sample. Note the amount
each stock has gained or lost in the last quarter.
Compute the mean and standard deviation for the data
set. Compute the 95% confidence interval for the mean
and the 95% confidence interval for the standard
deviation. Compute the percentage of stocks that had a
gain in the last quarter. Find a 95% confidence interval
for the percentage of stocks with a gain.

Step

Determ


Data

Chapter 10 Correlation and Regression

Answer the questions about the following computer-generated information.
Linear correlation coefficient r ϭ 0.794556
Coefficient of determination ϭ 0.631319
Standard error of estimate ϭ 12.9668
Explained variation ϭ 5182.41
Unexplained variation ϭ 3026.49
Total variation ϭ 8208.90
Equation of regression line
yЈ ϭ 0.725983X ϩ 16.5523
Level of significance ϭ 0.1
Test statistic ϭ 0.794556
Critical value ϭ 0.378419

42. Num
ber of Ru
represen
ns Made
t
The data
Bill Maz the number of ru
shown
eroski’s
career. Ch ns made each ye
30 59
ar during

eck for no
69
50
rmality.
36 13
29 17 58 71 55 43
Source:
3
66 52
Greensbu
rg Tribun
56 62
e Review.

4. Health and Wellness Use your class as the sample.
Have each student take her or his temperature on a
healthy day. Compute the mean and standard deviation
for the sample. Create a 95% confidence interval for
the mean temperature. Does the confidence interval
obtained support the long-held belief that the average
body temperature is 98.6ЊF?

Inspect th
e
the middl boxplot for outli
ers. Ther
e of the
ra
e
skewed

distribut nge, and the med are no outliers
in
ion eithe
ian is in
r.
the middl this graph. Furth
Calculat
er
e of the
e Pear
box. Mos more, the box is
son’s In
t likely th
in
The mea
de
x of Sk
is is not
sure of sk
ew
a
ne
ew
calculator
ss
and the fo ness in the graphi
cal summ
rmula.
ary is no
t the sam

PI ϭ 3ΘX Ϫ median Ι
e as Pear
son’s inde
x. Use th
s
e
3. Selec
t Calc
>Calculat
4. Enter
or
, then ty
the expr
pe PI in
es
sio
the parent
the text bo
n: 3*(M
EAN(C1
heses in
x
fo
r Store re
)؊MED
the right
5. Click
sult in:.
I(C1) )/(
place!

[OK]
STDEV(
smaller th . The result, 0.1
C1)). M
48318, wi
an ϩ1, th
ake sure
ll
you get all
e distrib
ution is no be stored in the
Constru
first row
ct a No
t skewed
of C2 na
rmal Pr
.
6. Selec
med PI.
obabili
ty Plot
t Graph
Since it
is
>Pr
ob
7. Doub
ability Pl
le-click

ot, then
C1 Inven
Single an
8. Click
tory to se
d click [O
[Distribut
lect the da
K]
.
ion
] and mak
ta to be gr
9. Click
e sure th
aphed.
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at Norm
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e subtitle.
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[OK] tw
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ice. Insp
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ect the g

Applying the Concepts are exercises found at the
end of each section to reinforce the concepts
explained in the section. They give the student an
opportunity to think about the concepts and apply
them to hypothetical examples similar to real-life
ones found in newspapers, magazines, and
professional journals. Most contain open-ended
questions—questions that require interpretation
and may have more than one correct answer. These
exercises can also be used as classroom discussion
topics for instructors who like to use this type of
teaching technique.

5. Politics and Economics Select five political polls and
note the margin of error, sample size, and percent
favoring the candidate for each. For each poll,
determine the level of confidence that must have been
used to obtain the margin of error given, knowing the
percent favoring the candidate and number of
participants. Is there a pattern that emerges?
6. Your Class Have each student compute his or her body
mass index (BMI) (703 times weight in pounds, divided
by the quantity height in inches squared). Find the mean
and standard deviation for the data set. Compute a 95%
confidence interval for the mean BMI of a student. A
BMI score over 30 is considered obese. Does the

confidence interval indicate that the mean for BMI
could be in the obese range?

Data Projects, which appear at the end of each chapter, further challenge students’ understanding and application of
the material presented in the chapter. Many of these require the student to gather, analyze, and report on real data.
xix


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Guided Tour: Features and Supplements

Multimedia
Supplements

MathZone— www.mhhe.com/bluman
McGraw-Hill’s MathZone is a complete online homework system for mathematics and
statistics. Instructors can assign textbook-specific content from over 40 McGraw-Hill
titles as well as customize the level of feedback students receive, including the ability to
have students show their work for any given exercise. Assignable content includes an
array of videos and other multimedia along with algorithmic exercises, providing study
tools for students with many different learning styles.

Within MathZone, a diagnostic assessment tool powered by ALEKS™ is available to
measure student preparedness and provide detailed reporting and personalized remediation.
MathZone also helps ensure consistent assignment delivery across several sections through
a course administration function and makes sharing courses with other instructors easy.
For additional study help students have access to NetTutor™, a robust online live
tutoring service that incorporates whiteboard technology to communicate mathematics.
The tutoring schedules are built around peak homework times to best accommodate student schedules. Instructors can also take advantage of this whiteboard by setting up a
Live Classroom for online office hours or a review session with students.
For more information, visit the book’s website (www.mhhe.com/bluman) or contact
your local McGraw-Hill sales representative (www.mhhe.com/rep).
ALEKS— www.aleks.com
ALEKS (Assessment and LEarning in Knowledge Spaces) is a dynamic online learning
system for mathematics education, available over the Web 24/7. ALEKS assesses students, accurately determines their knowledge, and then guides them to the material that
they are most ready to learn. With a variety of reports, Textbook Integration Plus, quizzes,
and homework assignment capabilities, ALEKS offers flexibility and ease of use for
instructors.
• ALEKS uses artificial intelligence to determine exactly what each student knows
and is ready to learn. ALEKS remediates student gaps and provides highly efficient
learning and improved learning outcomes.
• ALEKS is a comprehensive curriculum that aligns with syllabi or specified
textbooks. Used in conjunction with McGraw-Hill texts, students also receive links
to text-specific videos, multimedia tutorials, and textbook pages.
• Textbook Integration Plus allows ALEKS to be automatically aligned with syllabi
or specified McGraw-Hill textbooks with instructor chosen dates, chapter goals,
homework, and quizzes.
• ALEKS with AI-2 gives instructors increased control over the scope and sequence
of student learning. Students using ALEKS demonstrate a steadily increasing
mastery of the content of the course.
• ALEKS offers a dynamic classroom management system that enables instructors to
monitor and direct student progress towards mastery of course objectives.


ALEKS Prep for Statistics
ALEKS prep for Statistics can be used during the beginning of the course to prepare students for future success and to increase retention and pass rates. Backed by two decades
of National Science Foundation funded research, ALEKS interacts with students much
like a human tutor, with the ability to precisely assess a student’s preparedness and provide instruction on the topics the student is ready to learn.
ALEKS Prep for Statistics:
• Assists students in mastering core concepts that should have been learned prior to
entering the present course.


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Guided Tour: Features and Supplements

xxi

• Frees up lecture time for instructors, allowing more time to focus on current course
material and not review material.
• Provides up to six weeks of remediation and intelligent tutorial help to fill in
students’ individual knowledge gaps.

Electronic Textbook
CourseSmart is a new way for faculty to find and review eTextbooks. It’s also a great
option for students who are interested in accessing their course materials digitally and

saving money. CourseSmart offers thousands of the most commonly adopted textbooks
across hundreds of courses from a wide variety of higher education publishers. It is the
only place for faculty to review and compare the full text of a textbook online, providing
immediate access without the environmental impact of requesting a print exam copy.
At CourseSmart, students can save up to 50% off the cost of a print book, reduce the
impact on the environment, and gain access to powerful Web tools for learning including
full text search, notes and highlighting, and e-mail tools for sharing notes between
classmates. www.CourseSmart.com
Computerized Test Bank (CTB) Online (instructors only)
The computerized test bank contains a variety of questions, including true/false, multiplechoice, short answer, and short problems requiring analysis and written answers. The testing material is coded by type of question and level of difficulty. The Brownstone Diploma®
system enables you to efficiently select, add, and organize questions, such as by type of
question or level of difficulty. It also allows for printing tests along with answer keys as well
as editing the original questions, and it is available for Windows and Macintosh systems.
Printable tests and a print version of the test bank can also be found on the website.
Lecture Videos
New lecture videos introduce concepts, definitions, theorems, formulas, and problemsolving procedures to help students better comprehend the topic at hand. These videos
are closed-captioned for the hearing-impaired, are subtitled in Spanish, and meet the
Americans with Disabilities Act Standards for Accessible Design. They can be found
online at www.mhhe.com/bluman and are also available on DVD.
Exercise Videos
In these videos the instructor works through selected exercises, following the solution
methodology employed in the text. Also included are tutorials for using the TI-83 Plus
and TI-84 Plus calculators, Excel, and MINITAB, presented in an engaging format for
students. These videos are closed-captioned for the hearing-impaired, are subtitled in
Spanish, and meet the Americans with Disabilities Act Standards for Accessible Design.
They can be found online at www.mhhe.com/bluman and are also available on DVD.
NetTutor
NetTutor is a revolutionary system that enables students to interact with a live tutor over
the Web by using NetTutor’s Web-based, graphical chat capabilities. Students can also
submit questions and receive answers, browse previously answered questions, and view

previous live chat sessions. NetTutor can be accessed through MathZone.
MINITAB Student Release 14
The student version of MINITAB statistical software is available with copies of the text.
Ask your McGraw-Hill representative for details.


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Guided Tour: Features and Supplements

SPSS Student Version for Windows
A student version of SPSS statistical software is available with copies of this text. Consult
your McGraw-Hill representative for details.

Print
Supplements

Annotated Instructors Edition (instructors only)
The Annotated Instructor’s Edition contains answers to all exercises and tests. The
answers to most questions are printed in red next to each problem. Answers not appearing on the page can be found in the Answer Appendix at the end of the book.
Instructor’s Solutions Manual (instructors only)
By Sally Robinson of South Plains College, this manual includes worked-out solutions

to all the exercises in the text and answers to all quiz questions. This manual can be found
online at www.mhhe.com/bluman.
Student’s Solutions Manual
By Sally Robinson of South Plains College, this manual contains detailed solutions to all
odd-numbered text problems and answers to all quiz questions.
MINITAB 14 Manual
This manual provides the student with how-to information on data and file management,
conducting various statistical analyses, and creating presentation-style graphics while
following each text chapter.
TI-83 Plus and TI-84 Plus Graphing Calculator Manual
This friendly, practical manual teaches students to learn about statistics and solve problems
by using these calculators while following each text chapter.
Excel Manual
This workbook, specially designed to accompany the text, provides additional practice in
applying the chapter concepts while using Excel.


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Index of Applications
CHAPTE R

1


The Nature of Probability
and Statistics
Education and Testing
Attendance and Grades, 5
Piano Lessons Improve Math Ability, 31

Environmental Sciences, the Earth,
and Space
Statistics and the New Planet, 5

Medicine, Clinical Studies, and
Experiments
Beneficial Bacteria, 28
Caffeine and Health, 28
Smoking and Criminal Behavior, 31

Minimum Wage, 96
Workers Switch Jobs, 85
Working Women, 96

Demographics and Population
Characteristics
Boom in Number of Births, 87
Characteristics of the Population 65
and Over, 85
Counties, Divisions, or Parishes
for 50 States, 61
Distribution of Blood Types, 38
How People Get Their News, 95
Wealthiest People in the World, 37


Education and Testing

ACL Tears in Collegiate Soccer Players, 31

College Spending for First-Year
Students, 69
Do Students Need Summer
Development? 61
GRE Scores at Top-Ranked Engineering
Schools, 46
Making the Grade, 62
Math and Reading Achievement Scores, 86
Number of College Faculty, 61
Percentage Completing 4 Years of College, 95
Public Libraries, 97
Teacher Strikes, 100

Surveys and Culture

Entertainment

Psychology and Human Behavior
Anger and Snap Judgments, 31
Hostile Children Fight Unemployment, 31

Public Health and Nutrition
Are You Eating Your Fruits and Vegetables?
2, 29
Chewing Tobacco, 16


Sports, Exercise, and Fitness

Government, Taxes, Politics,
Public Policy, and Voting
How Much Paper Money is in Circulation
Today? 81
Percentage of Voters in Presidential
Elections, 85
Presidential Debates, 96
Presidential Vetoes, 47
State Gasoline Tax, 46

History
Ages of Declaration of Independence
Signers, 47
Ages of Presidents at Inauguration, 45, 86
Ages of Vice Presidents at the Time of Their
Death, 96
Delegates Who Signed the Declaration
of Independence, 84
JFK Assassination, 48

Law and Order: Criminal Justice
Arson Damage to Churches, 72
Car Thefts in a Large City, 82
Identity Fraud, 36, 97
Trial-Ready Cases, 96

Manufacturing and Product

Development
Meat Production, 86

American Culture and Drug Abuse, 13

Online Gambling, 47

Marketing, Sales, and Consumer
Behavior

Transportation

Environmental Sciences, the Earth,
and Space

Music Sales, 86

Commuting Times, 11
Safe Travel, 9
World’s Busiest Airports, 31
CHAPTER

2

Frequency Distributions
and Graphs
Buildings and Structures
Selling Real Estate, 60
Stories in Tall Buildings, 83
Stories in the World’s Tallest Buildings, 46


Business, Management, and Work
Career Changes, 96
Job Aptitude Test, 97

Air Quality Standards, 61
Average Global Temperatures, 85
Components of the Earth’s Crust, 85
Farm Data, 96
Heights of Alaskan Volcanoes, 47
Nuclear Power Reactors, 85
Record High Temperatures, 41
Successful Space Launches, 86
The Great Lakes, 100
U.S. National Park Acreage, 47
World Energy Use, 85

Food and Dining
Cost of Milk, 87
Super Bowl Snack Foods, 73
Water Usage, 99

Medicine, Clinical Studies,
and Experiments
BUN Count, 95
How Quick Are Dogs? 61
How Quick Are Older Dogs? 62
Leading Cause of Death, 83
Outpatient Cardiograms, 80
Quality of Health Care, 61


Public Health and Nutrition
Calories in Salad Dressings, 86
Cereal Calories, 62
Protein Grams in Fast Food, 62

Sports, Exercise, and Fitness
Ball Sales, 95
Home Run Record Breakers, 47

xxiii


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Index of Applications

Miles Run per Week, 57
NBA Champions, 96
NFL Franchise Values, 95
NFL Salaries, 61
Weights of the NBA’s Top 50 Players, 46

Women’s Softball Champions, 84

Percentage of Foreign-Born People
in the U.S., 120
Populations of Selected Cities, 119

Technology

Education and Testing

Economics and Investment
Investment Earnings, 174

Data Description

Achievement Test Scores, 154
College Room and Board Costs, 154
Elementary and Secondary Schools, 173
Enrollments for Selected Independent
Religiously Controlled 4-Year
Colleges, 120
Exam Grades, 175
Exam Scores, 139, 153
Expenditures per Pupil for Selected
States, 118
Final Grade, 121
Grade Point Average, 115, 118
Percentage of College-Educated Population
over 25, 120
Police Calls in Schools, 137

SAT Scores, 173
Starting Teachers’ Salaries, 138
Student Majors, 113
Teacher Salaries, 118, 153
Test Scores, 142, 144, 154, 155
Textbooks in Professors’ Offices, 174
Work Hours for College Faculty, 140

Buildings and Structures

Entertainment

Cell Phone Usage, 74
Internet Connections, 84

The Sciences
Nobel Prizes in Physiology or Medicine, 87
Twenty Days of Plant Growth, 86

Transportation
Automobile Fuel Efficiency, 61
MPGs for SUVs, 43
Top 10 Airlines, 86
Turnpike Costs, 70

Travel and Leisure
Airline Passengers, 47
Museum Visitors, 97
Reasons We Travel, 85
Roller Coaster Mania, 84


CHAPTE R

3

Deficient Bridges in U.S. States, 138
Prices of Homes, 135, 140
Stories in the Tallest Buildings, 138
Suspension Bridges, 139
Water-Line Breaks, 114

Business, Management, and Work
Average Earnings of Workers, 174
Average Weekly Earnings, 154
Coal Employees in Pennsylvania, 112
Commissions Earned, 120
Costs to Train Employees, 174
Employee Salaries, 125
Hourly Compensation for Production
Workers, 119
Hours Worked, 175
Labor Charges, 174
New Worth of Corporations, 120
Salaries of Personnel, 113
The Noisy Workplace, 166
Top-Paid CEOs, 119
Travel Allowances, 135
Years of Service of Employees, 174

Demographics and Population

Characteristics
Ages of Accountants, 139
Ages of Consumers, 140
Ages of the Top 50 Wealthiest People, 109
Median Household Incomes, 167

Earnings of Nonliving Celebrities, 118
Top Movie Sites, 175

Environmental Sciences, the Earth,
and Space
Ages of Astronaut Candidates, 138
Ages of U.S. Residents, 179
Cloudy Days, 111
Earthquake Strengths, 119
Farm Sizes, 140
Heights of the Highest Waterfalls, 118
High Temperatures, 118
Hurricane Damage, 155
Licensed Nuclear Reactors, 112
Number of Meteorites Found, 163
Number of Tornadoes, 168
Observers in the Frogwatch Program, 118
Precipitation and High Temperatures, 138
Rise in Tides, 173
Size of Dams, 167
Size of U.S. States, 138
Solid Waste Production, 140
State Sites for Frogwatch, 167
Tornadoes in 2005, 167

Tornadoes in the United States, 110
Unhealthful Smog Days, 168

Government, Taxes, Politics,
Public Policy, and Voting
Age of Senators, 153
Cigarette Taxes, 137

History
Years of Service of Supreme Court
Members, 174

Law and Order: Criminal Justice
Murders in Cities, 139
Murder Rates, 139

Manufacturing and Product
Development
Battery Lives, 139, 173
Comparison of Outdoor Paint, 123
Copier Service Calls, 120
Lightbulb Lifetimes, 139
Word Processor Repairs, 139

Marketing, Sales, and Consumer
Behavior
Automobile Sales, 132
Average Cost of Smoking, 178
Average Cost of Weddings, 178
Cost per Load of Laundry Detergents, 120, 138

Delivery Charges, 174
European Auto Sales, 129
Magazines in Bookstores, 174
Magazines Purchased, 111

Medicine, Clinical Studies,
and Experiments
Blood Pressure, 137
Determining Dosages, 153
Number of Cavities, 174
Number of Hospitals, 173
Serum Cholesterol Levels, 140
Systolic Blood Pressure, 146

Psychology and Human Behavior
Reaction Times, 139
Trials to Learn a Maze, 140

Public Health and Nutrition
Calories, 140
Fat Grams, 121
Sodium Content of Cheese, 164

Sports, Exercise, and Fitness
Baseball Team Batting Averages, 138
Earned Run Average and Number of Games
Pitched, 167
Home Runs, 138
Innings Pitched, 167
Miles Run Per Week, 107

NFL Salaries, 174
NFL Signing Bonuses, 111

Technology
Time Spent Online, 140

Food and Dining

Transportation

Citrus Fruit Consumption, 140
Diet Cola Preference, 121

Airplane Speeds, 154
Automobile Fuel Efficiency, 119, 139