Introduction
to Statistics
and Data Analysis


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Introduction
to Statistics
and Data Analysis
Third Edition
Roxy Peck
California Polytechnic State University, San Luis Obispo

Chris Olsen
George Washington High School, Cedar Rapids, IA

Jay Devore
California Polytechnic State University, San Luis Obispo

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Introduction to Statistics and Data Analysis,
Third Edition
Roxy Peck, Chris Olsen, Jay Devore

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To my nephews, Jesse and Luke Smidt, who
bet I wouldn’t put their names in this book.
R. P.

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To my wife, Sally, and my
daughter, Anna
C. O.

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To Carol, Allie, and Teri.
J. D.


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About the Authors

ROXY PECK is Associate Dean of the
College of Science and Mathematics
and Professor of Statistics at California
Polytechnic State University, San Luis
Obispo. Roxy has been on the faculty
at Cal Poly since 1979, serving for six
years as Chair of the Statistics Department before
becoming Associate Dean. She received an M.S. in
Mathematics and a Ph.D. in Applied Statistics from

the University of California, Riverside. Roxy is nationally known in the area of statistics education,
and in 2003 she received the American Statistical
Association’s Founder’s Award, recognizing her contributions to K–12 and undergraduate statistics education. She is a Fellow of the American Statistical
Association and an elected member of the International Statistics Institute. Roxy has recently completed five years as the Chief Reader for the Advanced Placement Statistics Exam and currently
chairs the American Statistical Association’s Joint
Committee with the National Council of Teachers of
Mathematics on Curriculum in Statistics and Probability for Grades K–12. In addition to her texts in introductory statistics, Roxy is also co-editor of Statistical Case Studies: A Collaboration Between Academe
and Industry and a member of the editorial board for
Statistics: A Guide to the Unknown, 4th edition. Outside the classroom and the office, Roxy likes to travel
and spends her spare time reading mystery novels.
She also collects Navajo rugs and heads to New Mexico whenever she can find the time.
CHRIS OLSEN has taught statistics
at George Washington High School in
Cedar Rapids, Iowa, for over 25 years.
Chris is a past member of the Advanced Placement Statistics Test Development Committee and the author
of the Teacher’s Guide for Advanced Placement Statistics. He has been a table leader at the AP Statistics
reading for 6 years and since the summer of 1996 has
been a consultant to the College Board. Chris leads
workshops and institutes for AP Statistics teachers
in the United States and internationally. Chris was
the Iowa recipient of the Presidential Award for Excellence in Science and Mathematics Teaching in
1986. He was a regional winner of the IBM Com-

puter Teacher of the Year award in 1988 and received
the Siemens Award for Advanced Placement in mathematics in 1999. Chris is a frequent contributor to
the AP Statistics Electronic Discussion Group and
has reviewed materials for The Mathematics Teacher,
the AP Central web site, The American Statistician,
and the Journal of the American Statistical Association. He currently writes a column for Stats magazine. Chris graduated from Iowa State University
with a major in mathematics and, while acquiring

graduate degrees at the University of Iowa, concentrated on statistics, computer programming, psychometrics, and test development. Currently, he divides
his duties between teaching and evaluation; in addition to teaching, he is the assessment facilitator for
the Cedar Rapids, Iowa, Community Schools. In his
spare time he enjoys reading and hiking. He and his
wife have a daughter, Anna, who is a graduate student in Civil Engineering at Cal Tech.
JAY DEVORE earned his undergraduate degree in Engineering Science from the University of California
at Berkeley, spent a year at the University of Sheffield in England, and finished his Ph.D. in statistics at Stanford
University. He previously taught at the University of
Florida and at Oberlin College and has had visiting
appointments at Stanford, Harvard, the University
of Washington, and New York University. From 1998
to 2006, Jay served as Chair of the Statistics Department at California Polytechnic State University, San
Luis Obispo. The Statistics Department at Cal Poly
has an international reputation for activities in statistics education. In addition to this book, Jay has
written several widely used engineering statistics
texts and is currently working on a book in applied
mathematical statistics. He is the recipient of a distinguished teaching award from Cal Poly and is a
Fellow of the American Statistical Association. In
his spare time, he enjoys reading, cooking and eating
good food, tennis, and travel to faraway places. He is
especially proud of his wife, Carol, a retired elementary school teacher, his daughter Allison, who works
for the Center for Women and Excellence in Boston,
and his daughter Teri, who is finishing a graduate
program in education at NYU.


Contents

1 The Role of Statistics and the Data Analysis Process


1

1.1 Three Reasons to Study Statistics 1
1.2 The Nature and Role of Variability 4
1.3 Statistics and the Data Analysis Process 7
1.4 Types of Data and Some Simple Graphical Displays 12
Activity 1.1 Head Sizes: Understanding Variability 22
Activity 1.2 Estimating Sizes 23
Activity 1.3 A Meaningful Paragraph 24

2 Collecting Data Sensibly

27

2.1 Statistical Studies: Observation and Experimentation 27
2.2 Sampling 32
2.3 Simple Comparative Experiments 42
2.4 More on Experimental Design 51
2.5 More on Observational Studies: Designing Surveys (Optional) 56
2.6 Interpreting and Communicating the Results of
Statistical Analyses 61
Activity 2.1 Designing a Sampling Plan 63
Activity 2.2 An Experiment to Test for the Stroop Effect 64
Activity 2.3 McDonald’s and the Next 100 Billion Burgers 64
Activity 2.4 Video Games and Pain Management 65
Graphing Calculator Explorations 69

3 Graphical Methods for Describing Data

75


3.1 Displaying Categorical Data: Comparative Bar Charts
and Pie Charts 76
3.2 Displaying Numerical Data: Stem-and-Leaf Displays 87

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Contents

3.3 Displaying Numerical Data: Frequency Distributions
and Histograms 97
3.4 Displaying Bivariate Numerical Data 117
3.5 Interpreting and Communicating the Results of
Statistical Analyses 127
Activity 3.1 Locating States 134
Activity 3.2 Bean Counters! 134
Graphing Calculator Explorations 141

4 Numerical Methods for Describing Data

147

4.1 Describing the Center of a Data Set 148
4.2 Describing Variability in a Data Set 159
4.3 Summarizing a Data Set: Boxplots 169

4.4 Interpreting Center and Variability: Chebyshev’s Rule,
the Empirical Rule, and z Scores 176
4.5 Interpreting and Communicating the Results of
Statistical Analyses 186
Activity 4.1 Collecting and Summarizing Numerical Data 190
Activity 4.2 Airline Passenger Weights 190
Activity 4.3 Boxplot Shapes 190
Graphing Calculator Explorations 195

5 Summarizing Bivariate Data

199

5.1 Correlation 200
5.2 Linear Regression: Fitting a Line to Bivariate Data 210
5.3 Assessing the Fit of a Line 221
5.4 Nonlinear Relationships and Transformations 238
5.5 Logistic Regression (Optional) 255
5.6 Interpreting and Communicating the Results
of Statistical Analyses 264
Activity 5.1 Exploring Correlation and Regression 267
Activity 5.2 Age and Flexibility 268
Graphing Calculator Explorations 272

6 Probability

279

6.1 Chance Experiments and Events 279
6.2 Definition of Probability 288



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Contents ix

6.3 Basic Properties of Probability 295
6.4 Conditional Probability 302
6.5 Independence 313
6.6 Some General Probability Rules 323
6.7 Estimating Probabilities Empirically Using Simulation 335
Activity 6.1 Kisses 347
Activity 6.2 A Crisis for European Sports Fans? 347
Activity 6.3 The “Hot Hand” in Basketball 347
Graphing Calculator Explorations 351

7 Random Variables and Probability Distributions

357

7.1 Random Variables 358
7.2 Probability Distributions for Discrete Random Variables 361
7.3 Probability Distributions for Continuous Random Variables 367
7.4 Mean and Standard Deviation of a Random Variable 372
7.5 Binomial and Geometric Distributions 386
7.6 Normal Distributions 397
7.7 Checking for Normality and Normalizing Transformations 414
7.8 Using the Normal Distribution to Approximate a
Discrete Distribution 425
Activity 7.1 Rotten Eggs? 429

Graphing Calculator Explorations 434

8 Sampling Variability and Sampling Distributions

445

8.1 Statistics and Sampling Variability 446
8.2 The Sampling Distribution of a Sample Mean 450
8.3 The Sampling Distribution of a Sample Proportion 461
Activity 8.1 Do Students Who Take the SATs Multiple Times Have
an Advantage in College Admissions? 468
Graphing Calculator Explorations 471

9 Estimation Using a Single Sample

475

9.1 Point Estimation 476
9.2 Large-Sample Confidence Interval for a Population Proportion 482
9.3 Confidence Interval for a Population Mean 495


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Contents

9.4 Interpreting and Communicating the Results of
Statistical Analyses 508

Activity 9.1 Getting a Feel for Confidence Level 514
Activity 9.2 An Alternative Confidence Interval for a
Population Proportion 515
Activity 9.3 Verifying Signatures on a Recall Petition 516
Activity 9.4 A Meaningful Paragraph 516
Graphing Calculator Explorations 521

10 Hypothesis Testing Using a Single Sample

525

10.1 Hypotheses and Test Procedures 526
10.2 Errors in Hypotheses Testing 531
10.3 Large-Sample Hypothesis Tests for a Population Proportion 537
10.4 Hypotheses Tests for a Population Mean 550
10.5 Power and Probability of Type II Error 562
10.6 Interpreting and Communicating the Results of
Statistical Analyses 571
Activity 10.1 Comparing the t and z Distributions 574
Activity 10.2 A Meaningful Paragraph 575
Graphing Calculator Explorations 580

11 Comparing Two Populations or Treatments

583

11.1 Inferences Concerning the Difference Between Two Population
or Treatment Means Using Independent Samples 583
11.2 Inferences Concerning the Difference Between Two Population
or Treatment Means Using Paired Samples 606

11.3 Large Sample Inferences Concerning a Difference Between Two
Population or Treatment Proportions 619
11.4 Interpreting and Communicating the Results of
Statistical Analyses 629
Activity 11.1 Helium-Filled Footballs 632
Activity 11.2 Thinking About Data Collection 633
Activity 11.3 A Meaningful Paragraph 633
Graphing Calculator Explorations 641


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Contents xi

12 The Analysis of Categorical Data and Goodness-ofFit Tests 647
12.1 Chi-Square Tests for Univariate Data 647
12.2 Tests for Homogeneity and Independence in a Twoway Table 660
12.3 Interpreting and Communicating the Results of
Statistical Analyses 677
Activity 12.1 Pick a Number, Any Number . . . 680
Activity 12.2 Color and Perceived Taste 680
Graphing Calculator Explorations 685

13 Simple Linear Regression and Correlation:
Inferential Methods 689
13.1 Simple Linear Regression Model 690
13.2 Inferences About the Slope of the Population Regression Line 702
13.3 Checking Model Adequacy 713
13.4 Inferences Based on the Estimated Regression Line
(Optional) 725

13.5 Inferences About the Population Correlation Coefficient
(Optional) 734
13.6 Interpreting and Communicating the Results of
Statistical Analyses 737
Activity 13.1 Are Tall Women from “Big” Families? 739
Graphing Calculator Exploration 746

14 Multiple Regression Analysis

749

14.1 Multiple Regression Models 750
14.2 Fitting a Model and Assessing Its Utility 763
14.3 Inferences Based on an Estimated Model 14-1
14.4 Other Issues in Multiple Regression 14-13
14.5 Interpreting and Communicating the Results of
Statistical Analyses 14-26
Activity 14.1 Exploring the Relationship Between Number of
Predictors and Sample Size 780

Sections and/or chapter numbers in color can be found at www.thomsonedu.com/statistics/peck


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Contents

15 Analysis of Variance


783

15.1 Single-Factor ANOVA and the F Test 784
15.2 Multiple Comparisons 800
15.3 The F Test for a Randomized Block Experiment 15-1
15.4 Two-Factor ANOVA 15-9
15.5 Interpreting and Communicating the Results of
Statistical Analyses 15-22
Activity 15.1 Exploring Single-Factor ANOVA 808
Graphing Calculator Exploration 811

16 Nonparametric (Distribution-Free) Statistical Methods

16-1

16.1 Distribution-Free Procedures for Inferences About a Difference
Between Two Population or Treatment Means Using Independent
Samples (Optional) 16-1
16.2 Distribution-Free Procedures for Inferences About a Difference
Between Two Population or Treatment Means Using
Paired Samples 16-10
16.3 Distribution-Free ANOVA 16-23
Appendix A: Statistical Tables 813
Appendix B: References 833
Answers to Selected Odd-Numbered Exercises 835
Index I-1

Sections and/or chapter numbers in color can be found at www.thomsonedu.com/statistics/peck



Preface

I

n a nutshell, statistics is about understanding the role that variability plays in drawing conclusions based on data. Introduction to Statistics and Data Analysis, Third Edition develops this crucial understanding of variability through its focus on the data
analysis process.

An Organization That Reflects the Data Analysis Process
Students are introduced early to the idea that data analysis is a process that begins with
careful planning, followed by data collection, data description using graphical and
numerical summaries, data analysis, and finally interpretation of results. This process
is described in detail in Chapter 1, and the ordering of topics in the first ten chapters
of the book mirrors this process: data collection, then data description, then statistical
inference.
The logical order in the data analysis process can be pictured as shown in the following figure.

Step 1:
Acknowledging
Variability—
Collecting
Data Sensibly

Step 2:
Describing
Variability
in the Data—
Descriptive
Statistics


Step 3:
Drawing
Conclusions
in a Way That
Recognizes
Variability in
the Data

Unlike many introductory texts, Introduction to Statistics and Data Analysis,
Third Edition is organized in a manner consistent with the natural order of the data
analysis process:

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Preface

Step 1:
Acknowledging
Variability—
Collecting Data
Sensibly

Step 2:
Describing
Variability

in the Data—
Descriptive
Statistics

Chapters 1–2

Chapters 3–5

Probability Supports
the Connection

Chapters 6–7

Step 3:
Drawing
Conclusions
in a Way That
Recognizes
Variability in
the Data

Chapters 8–15

The Importance of Context and Real Data
Statistics is not about numbers; it is about data—numbers in context. It is the context
that makes a problem meaningful and something worth considering. For example, exercises that ask students to compute the mean of 10 numbers or to construct a dotplot
or boxplot of 20 numbers without context are arithmetic and graphing exercises. They
become statistics problems only when a context gives them meaning and allows for interpretation. While this makes for a text that may appear “wordy” when compared to
traditional mathematics texts, it is a critical and necessary component of a modern statistics text.
Examples and exercises with overly simple settings do not allow students to practice interpreting results in authentic situations or give students the experience necessary to be able to use statistical methods in real settings. We believe that the exercises

and examples are a particular strength of this text, and we invite you to compare the
examples and exercises with those in other introductory statistics texts.
Many students are skeptical of the relevance and importance of statistics. Contrived problem situations and artificial data often reinforce this skepticism. A strategy
that we have employed successfully to motivate students is to present examples and
exercises that involve data extracted from journal articles, newspapers, and other published sources. Most examples and exercises in the book are of this nature; they cover
a very wide range of disciplines and subject areas. These include, but are not limited
to, health and fitness, consumer research, psychology and aging, environmental research, law and criminal justice, and entertainment.

A Focus on Interpretation and Communication
Most chapters include a section titled “Interpreting and Communicating the Results of
Statistical Analyses.” These sections include advice on how to best communicate the
results of a statistical analysis and also consider how to interpret statistical summaries


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Preface xv

found in journals and other published sources. A subsection titled “A Word to the
Wise” reminds readers of things that must be considered in order to ensure that statistical methods are employed in reasonable and appropriate ways.

Consistent with Recommendations for the Introductory Statistics Course Endorsed
by the American Statistical Association
In 2005, the American Statistical Association endorsed the report “College Guidelines
in Assessment and Instruction for Statistics Education (GAISE Guidelines),” which
included the following six recommendations for the introductory statistics course:
1.
2.
3.
4.

5.
6.

Emphasize statistical literacy and develop statistical thinking.
Use real data.
Stress conceptual understanding rather than mere knowledge of procedures.
Foster active learning in the classroom.
Use technology for developing conceptual understanding and analyzing data.
Use assessments to improve and evaluate student learning.

Introduction to Statistics and Data Analysis, Third Edition is consistent with these recommendations and supports the GAISE guidelines in the following ways:
1. Emphasize statistical literacy and develop statistical thinking.
Statistical literacy is promoted throughout the text in the many examples and exercises that are drawn from the popular press. In addition, a focus on the role of variability, consistent use of context, and an emphasis on interpreting and communicating results in context work together to help students develop skills in statistical
thinking.
2. Use real data.
The examples and exercises from Introduction to Statistics and Data Analysis,
Third Edition are context driven and reference sources that include the popular
press as well as journal articles.
3. Stress conceptual understanding rather than mere knowledge of procedures.
Nearly all exercises in Introduction to Statistics and Data Analysis, Third Edition
are multipart and ask students to go beyond just computation. They focus on interpretation and communication, not just in the chapter sections specifically devoted
to this topic, but throughout the text. The examples and explanations are designed
to promote conceptual understanding. Hands-on activities in each chapter are also
constructed to strengthen conceptual understanding. Which brings us to . . .
4. Foster active learning in the classroom.
While this recommendation speaks more to pedagogy and classroom practice, Introduction to Statistics and Data Analysis, Third Edition provides 33 hands-on activities in the text and additional activities in the accompanying instructor resources
that can be used in class or assigned to be completed outside of class. In addition,
accompanying online materials allow students to assess their understanding and develop a personalized learning plan based on this assessment for each chapter.
5. Use technology for developing conceptual understanding and analyzing data.
The computer has brought incredible statistical power to the desktop of every investigator. The wide availability of statistical computer packages such as MINITAB,

S-Plus, JMP, and SPSS, and the graphical capabilities of the modern microcomputer have transformed both the teaching and learning of statistics. To highlight the
role of the computer in contemporary statistics, we have included sample output


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Preface

throughout the book. In addition, numerous exercises contain data that can easily
be analyzed by computer, though our exposition firmly avoids a presupposition that
students have access to a particular statistical package. Technology manuals for
specific packages, such as MINITAB and SPSS, are available in the online materials that accompany this text.
The appearance of hand-held calculators with significant statistical and graphing capability has also changed statistics instruction in classrooms where access to
computers is still limited. The computer revolution of a previous generation is now
being writ small—or, possibly we should say, smaller—for the youngest generation
of investigators. There is not, as we write, anything approaching universal or even
wide agreement about the proper role for the graphing calculator in college statistics classes, where access to a computer is more common. At the same time, for
tens of thousands of students in Advanced Placement Statistics in our high schools,
the graphing calculator is the only dependable access to statistical technology.
This text allows the instructor to balance the use of computers and calculators
in a manner consistent with his or her philosophy and presents the power of the calculator in a series of Graphing Calculator Explorations. These are placed at the end
of each chapter, unobtrusive to those instructors whose technology preference is
the computer while still accessible to those instructors and students comfortable
with graphing calculator technology. As with computer packages, our exposition
avoids assuming the use of a particular calculator and presents the calculator capabilities in a generic format; specifically, we do not teach particular keystroke sequences, believing that the best source for such specific information is the calculator manual. For those using a TI graphing calculator, there is a technology manual
available in the online materials that accompany this text. As much as possible, the
calculator explorations are independent of each other, allowing instructors to pick
and choose calculator topics that are more relevant to their particular courses.

6. Use assessments to improve and evaluate student learning.
Assessment materials in the form of a test bank, quizzes, and chapter exams are
available in the instructor resources that accompany this text. The items in the test
bank reflect the data-in-context philosophy of the text’s exercises and examples.

Advanced Placement Statistics
We have designed this book with a particular eye toward the syllabus of the Advanced
Placement Statistics course and the needs of high school teachers and students. Concerns expressed and questions asked in teacher workshops and on the AP Statistics Electronic Discussion Group have strongly influenced our exposition of certain topics, especially in the area of experimental design and probability. We have taken great care to
provide precise definitions and clear examples of concepts that Advanced Placement
Statistics instructors have acknowledged as difficult for their students. We have also expanded the variety of examples and exercises, recognizing the diverse potential futures
envisioned by very capable students who have not yet focused on a college major.

Topic Coverage
Our book can be used in courses as short as one quarter or as long as one year in duration. Particularly in shorter courses, an instructor will need to be selective in deciding which topics to include and which to set aside. The book divides naturally into four
major sections: collecting data and descriptive methods (Chapters 1–5), probability
material (Chapters 6–8), the basic one- and two-sample inferential techniques (Chapters 9–12), and more advanced inferential methodology (Chapters 13–16). We include


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Preface xvii

an early chapter (Chapter 5) on descriptive methods for bivariate numerical data. This
early exposure raises questions and issues that should stimulate student interest in the
subject; it is also advantageous for those teaching courses in which time constraints
preclude covering advanced inferential material. However, this chapter can easily be
postponed until the basics of inference have been covered, and then combined with
Chapter 13 for a unified treatment of regression and correlation.
With the possible exception of Chapter 5, Chapters 1–10 should be covered in order. We anticipate that most instructors will then continue with two-sample inference
(Chapter 11) and methods for categorical data analysis (Chapter 12), although regression could be covered before either of these topics. Optional portions of Chapter 14

(multiple regression) and chapter 15 (analysis of variance) and Chapter 16 (nonparametric methods) are included in the online materials that accompany this text.

A Note on Probability
The content of the probability chapters is consistent with the Advanced Placement Statistics course description. It includes both a traditional treatment of probability and
probability distributions at an introductory level, as well as a section on the use of simulation as a tool for estimating probabilities. For those who prefer a briefer and more
informal treatment of probability, the book Statistics: The Exploration and Analysis of
Data, by Roxy Peck and Jay Devore, may be a more appropriate choice. Except for
the treatment of probability and the omission of the Graphing Calculator Explorations,
it parallels the material in this text. Please contact your sales rep for more information
about this alternative and other alternative customized options available to you.

New to This Edition
There are a number of changes in the Third Edition, including the following:
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More than 80 new examples and more than 180 new exercises that use data
from current journals and newspapers are included. In addition, more of the
exercises specifically ask students to write (for example, by requiring students to
explain their reasoning, interpret results, and comment on important features of an
analysis).

Examples and exercises that make use of data sets that can be accessed online from the text website are designated by an icon in the text, as are examples that are further illustrated in the technology manuals (MINITAB, SPSS,
etc.) that are available in the online materials that accompany this text.
More than 90 exercises have video solutions, presented by Brian Kotz of Montgomery College, which can be viewed online or downloaded for viewing later.
These exercises are designated by an icon in the text.
A number of new hands-on activities have been added to the end-of-chapter
activities. These activities can be used as a chapter capstone or can be integrated
at appropriate places as the chapter material is covered in class.
Students can now go online to test their understanding of the material covered
in each chapter and develop a personalized learning plan to assist them in addressing any areas of weakness.
A detailed description of the data analysis process now appears in Chapter 1.
Although the order of topics in the text generally mirrors the data collection
process with methods of data collection covered first, two graphical displays (dotplots and bar charts) are covered in Chapter 1 so that these simple graphical analysis tools can be used in the conceptual development of experimental design and so


xviii

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Preface

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that students have some tools for summarizing the data they collect through sampling and experimentation in the exercises, examples, and activities of Chapter 2.
A new optional section on logistic regression is now included in Chapter 5 for

those who would like more complete coverage of data analysis techniques for categorical data.
Advanced topics that are often omitted in a one-quarter or one-semester course,
such as inference and variable selection methods in multiple regression (Sections
14.3 and 14.4) and analysis of variance for randomized block and two-factor designs (Sections 15.3 and 15.4), have been moved to the online materials that
accompany this text.
Coverage of distribution-free procedures for inferences about the difference
between two population or treatment means using independent samples (formerly
Section 11.4) has been moved to Chapter 16. This chapter, titled “Nonparametric
(Distribution-Free) Statistical Methods,” also includes new material on inferences
about the difference between two population or treatment means using paired
samples and distribution-free analysis of variance, and is available in the online
materials that accompany this text.
Updated materials for instructors. In addition to the usual instructor supplements such as a complete solutions manual and a test bank, the following are also
available to instructors:
An Instructor’s Resource Binder, which contains additional examples that
can be incorporated into classroom presentations and cross-references to resources such as Fathom, Workshop Statistics, and Against All Odds. Of particular interest to those teaching Advanced Placement Statistics, the binder also
includes additional data analysis questions of the type encountered on the free
response portion of the Advanced Placement exam, as well as a collection of
model responses.
For those who use student response systems in class, a set of “clicker” questions (see JoinIn™ on TurningPoint ® under Instructor Resources—Media) for
assessing student understanding is available.
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Student Resources
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Available Online
If your text includes a printed access card, you will have instant access to the following resources referenced throughout your text:

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ThomsonNOW™ (see below for a full description of this powerful study tool).
Complete step-by-step instructions for MINITAB, Excel, TI-83 Graphing Calculator, JMP, and SPSS indicated by the icon throughout the text.
Data sets formatted for MINITAB, Excel, SPSS, SAS, JMP, TI-83, Fathom, and
ASCII indicated by ● icon throughout the text.
Applets used in the Activities found in the text.

Print
Student Solutions Manual (ISBN 0-495-11876-1) by Mary Mortlock of California
Polytechnic State University, San Luis Obispo.
Check your work—and your understanding—with this manual, which provides
worked-out solutions to the odd-numbered problems in the text.


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Preface xix

Activities Workbook (0-495-11883-4) by Roxy Peck.
Use this convenient workbook to take notes, record data, and cement your learning by
completing textbook and bonus activities for each chapter.
■


Media
ThomsonNOW™ Homework (0-495-39230-8)
Save time, learn more, and succeed in the course with this online suite of resources
(including an integrated eBook and Personalized Study plans) that give you the choices
and tools you need to study smarter and get the grade. Note: If your text did not include a printed access card for ThomsonNOW, it is available for purchase online at
.

Instructor Resources
■

Print
Annotated Instructor’s Edition (0-495-11888-5)
The Annotated Instructor’s Edition contains answers for all exercises, as well as an annotated table of contents with comments written by Roxy Peck.
Instructor’s Solutions Manual (0-495-11879-6) by Mary Mortlock of California
Polytechnic State University, San Luis Obispo.
This manual contains worked-out solutions to all of the problems in the text.
Instructor’s Resource Binder (0-495-11892-3) prepared by Chris Olsen.
Includes transparencies and Microsoft ® PowerPoint ® slides to make lecture and class
preparation quick and easy. New to this edition, we have added some Activities Worksheets authored by Carol Marchetti of Rochester Institute of Technology.
Test Bank (0-495-11880-X) by Josh Tabor of Wilson High School, Peter FlannaganHyde of Phoenix Country Day School, and Chris Olsen.
Includes test questions for each section of the book.
Activities Workbook (0-495-11883-4) by Roxy Peck.
Students can take notes, record data, and complete activities in this ready-to-use workbook, which includes activities from the textbook plus additional bonus activities for
each chapter.

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Media
Enhanced WebAssign (ISBN 0-495-10963-0)

Enhanced WebAssign is the most widely used homework system in higher education. Available for this title, Enhanced WebAssign allows you to assign, collect, grade,
and record homework assignments via the web. This proven homework system has
been enhanced to include links to the textbook sections, video examples, and problemspecific tutorials. Enhanced WebAssign is more than a homework system—it is a complete learning system for students.
ThomsonNOW™ Homework (0-495-39230-8)
ThomsonNOW’s Personalized Study plans allow students to study smarter by diagnosing their weak areas, and helping them focus on what they need to learn. Based on
responses to chapter specific pre-tests, the plans suggest a course of study for students,


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Preface

including many multimedia and interactive exercises to help students better learn the
material. After completing the study plan, they can take a post-test to measure their
progress and understanding.
ExamView ® Computerized Testing (0-495-11886-9)
Create, deliver, and customize tests and study guides (both print and online) in minutes with this easy-to-use assessment and tutorial system, which contains all questions
from the Test Bank in electronic format.
JoinIn™ on TurningPoint ® (0-495-11881-8)
The easiest student classroom response system to use, JoinIn features instant classroom assessment and learning.

Acknowledgments
We are grateful for the thoughtful feedback from the following reviewers that has
helped to shape this text over the last two editions:
■

Reviewers of the Third Edition
Arun K. Agarwal

Grambling State University

Marvin Creech
Chapman University

Jacob Amidon
Finger Lakes Community College

Ron Degges
North Dakota State University

Holly Ashton
Pikes Peak Community College

Hemangini Deshmukh
Mercyhurst College

Barb Barnet
University of Wisconsin at
Platteville

Ann Evans
University of Massachusetts at
Boston
Central Carolina Community College

Eddie Bevilacqua
State University of New York
College of Environmental Science
& Forestry

Piotr Bialas
Borough of Manhattan Community
College
Kelly Black
Union College
Gabriel Chandler
Connecticut College
Andy Chang
Youngstown State University
Jerry Chen
Suffolk Community College
Richard Chilcoat
Wartburg College

Guangxiong Fang
Daniel Webster College
Sharon B. Finger
Nicholls State University
Steven Garren
James Madison University
Tyler Haynes
Saginaw Valley State University
Sonja Hensler
St. Petersburg College
Trish Hutchinson
Angelo State University
Bessie Kirkwood
Sweet Briar College
Jeff Kollath
Oregon State University



■

Preface xxi

Christopher Lacke
Rowan University

Michael I. Ratliff
Northern Arizona University

Michael Leitner
Louisiana State University

David R. Rauth
Duquesne University

Zia Mahmood
College of DuPage

Kevin J. Reeves
East Texas Baptist University

Art Mark
Georgoa Military College

Robb Sinn
North Georgia College & State
University


David Mathiason
Rochester Institute of Technology
Bob Mattson
Eureka College
C. Mark Miller
York College
Megan Mocko
University of Florida
Kane Nashimoto
James Madison University
Helen Noble
San Diego State University
Broderick Oluyede
Georgia Southern University
Elaine Paris
Mercy College
Shelly Ray Parsons
Aims Community College

Greg Sliwa
Broome Community College
Angela Stabley
Portland Community College
Jeffery D. Sykes
Ouachita Baptist University
Yolande Tra
Rochester Institute of Technology
Nathan Wetzel
University of Wisconsin Stevens

Point
Dr. Mark Wilson
West Virginia University Institute
of Technology
Yong Yu
Ohio State University
Toshiyuki Yuasa
University of Houston

Judy Pennington-Price
Midway College
Hazard Community College
Jackson County High School
■

Reviewers for the Second Edition
Jim Bohan
Manheim Township High School

John Imbrie
University of Virginia

Pat Buchanan
Pennsylvania State University

Pam Martin
Northeast Louisiana University

Mary Christman
American University

Iowa State University

Paul Myers
Woodward Academy

Mark Glickman
Boston University

Deanna Payton
Oklahoma State University


xxii

■

Preface

Michael Phelan
Chapman University

Lawrence D. Ries
University of Missouri Columbia

Alan Polansky
Northern Illinois University

Joe Ward
Health Careers High School


Additionally, we would like to express our thanks and gratitude to all who helped to
make this book possible:
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Carolyn Crockett, our editor and friend, for her unflagging support and thoughtful advice for more than a decade.
Danielle Derbenti, Beth Gershman, and Colin Blake at Thomson Brooks/Cole, for
the development of all of the ancillary materials details and for keeping us on
track.
Jennifer Risden, our project manager at Thomson Brooks/Cole, and Anne Seitz at
Hearthside Publishing Services, for artfully managing the myriad of details associated with the production process.
Nancy Dickson for her careful copyediting.
Brian Kotz for all his hard work producing the video solutions.
Mary Mortlock for her diligence and care in producing the student and instructor

solutions manuals for this book.
Josh Tabor and Peter Flannagan-Hyde for their contributions to the test bank that
accompanies the book.
Beth Chance and Francisco Garcia for producing the applet used in the confidence
interval activities.
Gary McClelland for producing the applets from Seeing Statistics used in the regression activities.
Bittner Development Group for checking the accuracy of the manuscript.
Rachel Dagdagan, a student at Cal Poly, for her help in the preparation of the
manuscript.

And, as always, we thank our families, friends, and colleagues for their continued
support.
Roxy Peck
Chris Olsen
Jay Devore


Peck, Olsen, Devore’s

Introduction to Statistics and Data Analysis, Third Edition . . .

. . . Emphasizes
Statistical Literacy and
Statistical Thinking
■ E x e r c i s e s 2.1–2.9 ..............................

▲

Context Driven
Applications


2.1 ▼ The article “Television’s Value to Kids: It’s All in
How They Use It” (Seattle Times, July 6, 2005) described
a study in which researchers analyzed standardized test results and television viewing habits of 1700 children. They
found that children who averaged more than two hours of
television viewing per day when they were younger than
3 tended to score lower on measures of reading ability and
short term memory.
a. Is the study described an observational study or an
experiment?
b. Is it reasonable to conclude that watching two or more
hours of television is the cause of lower reading scores?
Explain.

Real data examples and exercises
throughout the text are drawn from the
popular press, as well as journal articles.

Page 31

..........................................................................................................................................

E x a m p l e 3 . 2 2 Education Level and Income—Stay in School!
The time-series plot shown in Figure 3.34 appears on the U.S. Census Bureau web
site. It shows the average earnings of workers by educational level as a proportion of
the average earnings of a high school graduate over time. For example, we can see
from this plot that in 1993 the average earnings for people with bachelor’s degrees
was about 1.5 times the average for high school graduates. In that same year, the average earnings for those who were not high school graduates was only about 75%

Focus on Interpreting

and Communicating

used in reasonable and appropriate
ways.

■

Page 123

▲

Chapter sections on interpreting and
communicating results are designed to
emphasize the importance of being
able to interpret statistical output and
communicate its meaning to nonstatisticians. A subsection entitled “A
Word to the Wise” reminds students of
things that must be considered in order
to ensure that statistical methods are

........................................................................................................................................

4.5

Interpreting and Communicating the Results of
Statistical Analyses
As was the case with the graphical displays of Chapter 3, the primary function of the
descriptive tools introduced in this chapter is to help us better understand the variables
under study. If we have collected data on the amount of money students spend on textbooks at a particular university, most likely we did so because we wanted to learn
about the distribution of this variable (amount spent on textbooks) for the population

of interest (in this case, students at the university). Numerical measures of center and
spread and boxplots help to enlighten us, and they also allow us to communicate to
others what we have learned from the data.

A Word to the Wise: Cautions and Limitations ...............................................
When computing or interpreting numerical descriptive measures, you need to keep in
mind the following:

Page 186

1. Measures of center don’t tell all. Although measures of center, such as the mean
and the median, do give us a sense of what might be considered a typical value for
a variable, this is only one characteristic of a data set. Without additional information about variability and distribution shape, we don’t really know much about the
behavior of the variable.
2. Data distributions with different shapes can have the same mean and standard deviation. For example, consider the following two histograms:

Page 188
xxiii


Peck, Olsen, Devore’s

Introduction to Statistics and Data Analysis, Third Edition . . .

. . . Encourages
Conceptual Understanding
and Active Learning

Thirty-three hands-on activities
in the text, and additional activities

in the accompanying instructor resources, can be used to encourage active learning inside or
outside the classroom.

▲

Hands-on Activities in
Every Chapter

A c t i v i t y 2.4

Video Games and Pain Management

Background: Video games have been used for pain management by doctors and therapists who believe that the
attention required to play a video game can distract the
player and thereby decrease the sensation of pain. The paper “Video Games and Health” (British Medical Journal
[2005]:122–123) states:
“However, there has been no long term follow-up and
no robust randomized controlled trials of such interventions. Whether patients eventually tire of such
games is also unclear. Furthermore, it is not known
whether any distracting effect depends simply on concentrating on an interactive task or whether the content of games is also an important factor as there have
been no controlled trials comparing video games with
other distracters. Further research should examine
factors within games such as novelty, users’ preferences, and relative levels of challenge and should

compare video games with other potentially distracting activities.”
1. Working with a partner, select one of the areas of potential research suggested in the passage from the paper
and formulate a specific question that could be addressed
by performing an experiment.
2. Propose an experiment that would provide data to address the question from Step 1. Be specific about how
subjects might be selected, what the experimental conditions (treatments) would be, and what response would be

measured.
3. At the end of Section 2.3 there are 10 questions that
can be used to evaluate an experimental design. Answer
these 10 questions for the design proposed in Step 2.
4. After evaluating your proposed design, are there
any changes you would like to make to your design?
Explain.

Page 65

E x p l o r a t i o n 3.3 Scaling the Histogram

Figure 3.44

Page 144

xxiv

▲

Figure 3.43

When we constructed a histogram in the previous Exploration there were some numbers that we temporarily ignored in the view screen. We would like to return to those
numbers now because they can seriously affect the look of a histogram. When we left
the histogram the numbers in our view window were set as shown in Figure 3.43.
These settings place the view window over the calculator’s Cartesian system for effective viewing of the histogram from the data of Example 3.15.
We would now like to experiment a bit with the “Xscale.” In all statistical graphs
produced by the calculator the Xscale and Yscale choices will control the placement
of the little “tick” marks on the x and y axis. In Exploration 3.2, the XScale and YScale
were set at 5 and 1, respectively. The little tick marks on the x-axis were at multiples

of 5. (Because of the data, the x-axis tick marks were at multiples of 5 and the y-axis
didn’t appear.) Change the Xscale value to 2 and redraw the histogram. You should see
a graph similar to Figure 3.44. The y-axis tick marks now appear at multiples of 2, . . . .
Note that changing the Xscale has altered not only the tick marks but also the class
intervals for the histogram. The choice of class intervals can significantly change the
look and feel of the histogram. The choice of Xscale can affect judgments about the
shape of the histogram. Because of this possibility it is wise to look at a histogram
with varying choices of the Xscale value. If the shape appears very similar for different choices of Xscale, you can interpret and describe the shape with more confidence.
However, if different Xscale choices alter the look of the histogram you should probably be more tentative.

Graphing Calculator
Explorations
Found at the end of most chapters,
these explorations allow students to
actively experience technology
and promote statistical thinking.