A01 DEVE8422 02 SE FM.indd Page 1 9/24/14 9:42 AM · PDF fileCover Designer: Anthony Leung ... is the author and designer of the multimedia Statistics program ActivStats, ... New York

RichaRd d. de Veaux Paul f. Velleman daVid e. Bock Williams College Cornell University Cornell University

augustin m. VukoV augustine c.m. Wong University of toronto york University

With contributions from Craig Burkett, University of Toronto

toronto

s e C o n D C a n a D i a n e D i t i o n

statsData anD moDels

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Credits and acknowledgments for material borrowed from other sources and reproduced, with permission, in this textbook appear on the appropriate page within the text.

Original edition published by Pearson Education, Inc., Upper Saddle River, New Jersey, USA. Copyright © 2013 Pearson Education, Inc. This edition is authorized for sale only in Canada.

If you purchased this book outside the United States or Canada, you should be aware that it has been imported without the approval of the publisher or the author.

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Library and Archives Canada Cataloguing in Publication

De Veaux, Richard D., author Stats : data and models/Richard D. De Veaux, Williams College, Paul F. Velleman, Cornell University, David E. Bock, Cornell University, Augustin M. Vukov, University of Toronto, Augustine C.M. Wong, York University.—Second Canadian edition. Includes index. Revision of: Stats : data and models / Richard D. De Veaux . . . [et al.].— Canadian ed.—Toronto : Pearson Canada, © 2012. ISBN 978-0-321-82842-2 (bound) 1. Statistics—Textbooks. 2. Mathematical statistics—Textbooks. I. Velleman, Paul F., 1949–, author II. Bock, David E., author III. Vukov, Augustin M., author IV. Wong, Augustine C. M., author V. Title.

QA276.12.S74 2014 519.5 C2014-905255-3

1 2 3 4 5 6 7 8 9 10 [CKV]

ISBN 978-0-321-8284-2


To Sylvia, who has helped me in more ways than she’ll ever know, and to Nicholas, Scyrine, Frederick, and Alexandra,

who make me so proud in everything that they are and do

—Dick

To my sons, David and Zev, from whom I’ve learned so much, and to my wife, Sue, for taking a chance on me

—Paul

To Greg and Becca, great fun as kids and great friends as adults, and especially to my wife and best friend, Joanna, for her

understanding, encouragement, and love

—Dave

To my adopted country, Canada, my vibrantly multicultural city of Toronto, and the University of Toronto without which this

dedication would never have happened.

—Gus

To anyone I may ever teach, that you may gain as much joy from learning Statistics as I have.

—Craig


Meet the Authors

Richard D. De Veaux is an internationally known educator and consultant. He has taught at the Wharton School and the Princeton University School of Engineering, where he won a “Lifetime Award for Dedication and Excellence in Teaching.” Since 1994, he has been Professor of Statistics at Williams College. Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality. He is a fellow of the American Statistical Association (ASA). In 2008, he was named Statistician of the Year by the Boston Chapter of the ASA. Dick is also well known in industry, where for more than 25 years he has consulted for such Fortune 500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. Because he consulted with Mickey Hart on his book Planet Drum, he has also sometimes been called the “Official Statistician for the Grateful Dead.” His real-world experiences and anecdotes illustrate many of this book’s chapters.

Dick holds degrees from Princeton University in Civil Engineering (B.S.E.) and Mathematics (A.B.) and from Stanford University in Dance Education (M.A.) and Statistics (Ph.D.), where he studied dance with Inga Weiss and Statistics with Persi Diaconis. His research focuses on the analysis of large data sets and data mining in science and industry.

In his spare time, he is an avid cyclist and swimmer. He also is the founder of the “Diminished Faculty,” an a cappella Doo-Wop quartet at Williams College and sings bass in the college concert choir. Dick is the father of four children.

Paul F. Velleman has an international reputation for innovative Statistics education. He is the author and designer of the multimedia Statistics program ActivStats, for which he was awarded the EDUCOM Medal for innovative uses of computers in teaching statistics, and the ICTCM Award for Innovation in Using Technology in College Mathematics. He also developed the award-winning statistics program, Data Desk, and the Internet site Data and Story Library (DASL) (lib.stat.cmu.edu/DASL/), which provides data sets for teaching Statistics. Paul’s understanding of using and teaching with technology informs much of this book’s approach.

Paul has taught Statistics at Cornell University since 1975. He holds an A.B. from Dartmouth College in Mathematics and Social Science, and M.S. and Ph.D. degrees in Statistics from Princeton University, where he studied with John Tukey. His research often deals with statistical graphics and data analysis methods. Paul co-authored (with David Hoaglin) ABCs of Exploratory Data Analysis. Paul is a Fellow of the American Statistical Association and of the American Association for the Advancement of Science. Paul is the father of two boys.

David E. Bock taught mathematics at Ithaca High School for 35 years. He has taught Statistics at Ithaca High School, Tompkins-Cortland Community College, Ithaca College, and Cornell University. Dave has won numerous teaching awards, including the MAA’s Edyth May Sliffe Award for Distinguished High School Mathematics Teaching (twice), Cornell University’s Outstanding Educator Award (three times), and has been a finalist for New York State Teacher of the Year.

Dave holds degrees from the University at Albany in Mathematics (B.A.) and Statistics/Education (M.S.). Dave has been a reader and table leader for the AP Statistics exam, serves as a Statistics consultant to the College Board, and leads workshops and institutes for AP Statistics teachers. He has recently served as K–12 Education and Outreach Coordinator and a senior lecturer for the Mathematics Department at Cornell University. His understand-ing of how students learn informs much of this book’s approach.

Dave and his wife relax by biking or hiking, spending much of their free time in Canada, the Rockies, or the Blue Ridge Mountains. They have a son, a daughter, and four grandchildren.

iv


Meet the Authors v

Augustin M. Vukov enjoys his daily walk to work at the University of Toronto, where he has studied and taught for several decades. During that time, one of his major responsi-bilities has been lecturing and coordinating for the large multi-section introductory service course—The Practice of Statistics—which serves the needs of nearly 1000 students per year, who come from a great variety of disciplines. Having taught so many students their required Stats course, he was not surprised to hear his new family doctor, his new ophthal-mologist and a prospective new dentist say “Your name looks familiar. Ah yes, I used to sit in your Statistics lecture!”. When not equipping future doctors and dentists with important tools, he enjoys travel and music, both preferably Brazilian, and summer softball, though he no longer gets to chase fly balls and smack home runs for the department, since the team has folded up shop. Oh well, more time to write!

Augustine C.M. Wong is a professor of Statistics at York University. He completed his Ph.D. at the University of Toronto in 1990 and taught at the University of Waterloo and University of Alberta before coming to York in 1993. His research interests include asymp-totic inference, computational methods in statistics, and likelihood-based methods. He is an author or co-author of over 60 research articles and 2 book chapters. At York, he teaches various statistics courses at both the undergraduate and graduate levels.

Craig Burkett is a former aerospace engineer and high school teacher who now teaches Statistics at the University of Toronto. He also works as a statistical consultant, providing advice and expertise to anyone who will listen to him. Having worked with engineers, doc-tors, marketing firms, airlines, educators, pharmaceutical companies, and a variety of other researchers, Craig really enjoys the variety statistical consulting provides. And the money.

When not crunching numbers or preparing classes, Craig can be found riding his bike, playing rugby or classical piano (though not at the same time) or trying to renovate his condo by himself. When not doing any of those things, he can be found trying to undo the damage he has done to his condo.


vi

Preface x

Part I Exploring and Understanding Data

1 Stats Starts Here 11.1 What Is Statistics? ■ 1.2 Data ■ 1.3 Variables

2 Displaying and Describing Categorical Data 142.1 One Categorical Variable ■ 2.2 Exploring the Relationship Between

Two Categorical Variables

3 Displaying and Summarizing Quantitative Data 443.1 Displaying Quantitative Variables with Graphs ■ 3.2 Describing

Quantitative Variables with Numbers

4 Understanding and Comparing Distributions 874.1 Comparing Groups ■ 4.2 Comparing Boxplots ■ 4.3 Outliers

4.4 Timeplots ■ 4.5 Re-expressing Data

5 The Standard Deviation as a Ruler and the Normal Model 1155.1 Standardizing with z-Scores ■ 5.2 Shifting and Scaling ■ 5.3 Density

Curves and the Normal Model ■ 5.4 Finding Normal Percentiles ■

5.5 Normal Probability Plots

Part I Review: Exploring and Understanding Data 146

Part II Exploring Relationships Between Variables

6 Scatterplots, Association, and Correlation 1586.1 Scatterplots ■ 6.2 Correlation ■ 6.3 Warning:

Correlation Z Causation ■ 6.4 Straightening Scatterplots

7 Linear Regression 1887.1 Least Squares: The Line of “Best Fit” ■ 7.2 The Linear Model ■

7.3 Finding the Least Squares Line ■ 7.4 Regression to the Mean ■

7.5 Examining the Residuals ■ 7.6 R2—The Variation Accounted For

by the Model ■ 7.7 Regression Assumptions and Conditions

8 Regression Wisdom 2218.1 Examining Residuals ■ 8.2 Outliers, Leverage, and Influence ■

8.3 Extrapolation: Reaching Beyond the Data ■ 8.4 Cautions

Part II Review: Exploring Relationships Between Variables 253

Contents


Contents vii

Part III Gathering Data

9 Understanding Randomness 2639.1 What Is Randomness? ■ 9.2 Simulating Randomness

10 Sample Surveys 27610.1 The Three Big Ideas of Sampling ■ 10.2 Populations and

Parameters ■ 10.3 Simple Random Samples ■ 10.4 Other Sampling

Designs ■ 10.5 Defining the “Who”: You Can’t Always Get What

You Want ■ 10.6 The Valid Survey ■ 10.7 Common Sampling Mistakes,

or How to Sample Badly

11 Experiments and Observational Studies 30211.1 Observational Studies ■ 11.2 Randomized, Comparative

Experiments ■ 11.3 The Four Principles of Experimental Design ■

11.4 Control Treatments ■ 11.5 Blocking ■ 11.6 Adding More

Factors ■ 11.7 Confounding

Part III Review: Gathering Data 330

Part IV Randomness and Probability

12 From Randomness to Probability 33612.1 Random Phenomena ■ 12.2 Modelling Probability ■

12.3 Formal Probability Rules

13 Probability Rules! 35313.1 Probability on Condition ■ 13.2 Independence ■ 13.3 Tables and

Conditional Probability ■ 13.4 Reversing the Conditioning and Bayes’ Rule

14 Random Variables 37614.1 Expected Value or Mean ■ 14.2 Variance and Standard Deviation ■

14.3 Combining Random Variables ■ 14.4 The Binomial Model ■

*14.5 The Poisson Model ■ 14.6 Continuous Models ■

14.7 Approximating the Binomial with a Normal Model

Part IV Review: Randomness and Probability 413

Part V From the Data at Hand to the World at Large

15 Sampling Distribution Models 41915.1 Sampling Distribution of a Proportion ■ 15.2 Assumptions and

Conditions ■ 15.3 The Sampling Distribution of Other Statistics ■

15.4 The Central Limit Theorem: The Fundamental Theorem of

Statistics ■ 15.5 Sampling Distribution Models

*Optional section.

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viii Contents

16 Confidence Intervals for Proportions 45116.1 A Confidence Interval ■ 16.2 Interpreting Confidence Intervals:

What Does “95% Confidence” Really Mean? ■ 16.3 Margin of Error:

Certainty versus Precision ■ 16.4 Assumptions and Conditions

■ *16.5 The Plus Four Confidence Interval for Small Samples

■ *16.6 Large Sample Confidence Intervals Based on Normally

Distributed Estimators

17 Testing Hypotheses About Proportions 47817.1 Hypotheses ■ 17.2 P-Values ■ 17.3 The Reasoning of Hypothesis

Testing ■ 17.4 Alternative Alternatives ■ 17.5 P-Values and Decisions:

What to Tell About a Hypothesis Test ■ 17.6 *Large Sample Tests of

Hypotheses Based on Normally Distributed Estimators

18 More About Tests 50218.1 Choosing the Hypotheses ■ 18.2 How to Think About P-Values

■ 18.3 Alpha Levels and Significance ■ 18.4 Critical Values for

Hypothesis Tests ■ 18.5 Decision Errors ■ 18.6 Power and Sample Size

19 Comparing Two Proportions 53319.1 The Standard Deviation of the Difference Between Two Proportions

■ 19.2 Assumptions and Conditions When Comparing Proportions

■ 19.3 A Confidence Interval for the Difference Between Two

Proportions ■ 19.4 Testing for a Difference Between Proportions

Part V Review: From the Data at Hand to the World at Large 556

Part VI Learning About the World

20 Inferences About Means 56320.1 The Central Limit Theorem Revisited ■ 20.2 Gosset’s t

■ 20.3 A t-Interval for the Mean ■ 20.4 Hypothesis Test for the

Mean ■ 20.5 Determining the Sample Size ■ *20.6 The Sign Test

21 Comparing Means 60021.1 Comparing Means of Independent Samples ■ 21.2 The Two-Sample

t-Test ■ 21.3 The Pooled t-Test ■ 21.4 Determining the Sample Size

22 Paired Samples and Blocks 63322.1 Paired t-Test ■ 22.2 Assumptions and Conditions ■ 22.3 Paired

t Confidence Interval ■ 22.4 Effect Size and Sample Size

■ 22.5 Blocking ■ *22.6 The Sign Test for Paired Data

23 Comparing Counts 65823.1 Goodness-of-Fit ■ 23.2 Comparing Observed Distributions

■ 23.3 Examining the Residuals ■ 23.4 Independence

■ 23.5 Chi-Square and Causation

Part VI Review: Learning About the World 689*Optional section.

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Contents ix

Part VII Inference When Variables Are Related

24 Inferences for Regression 70124.1 A Regression Model ■ 24.2 Standard Errors for the Parameters

■ 24.3 Regression Inference ■ 24.4 Inference for the Mean of y and for

a Future Value of y ■ 24.5 Correlation Test ■ *24.6 Logistic Regression

25 Analysis of Variance 74425.1 Inference for a Difference in Means ■ 25.2 The ANOVA Model

■ 25.3 Assumptions and Conditions ■ 25.4 Comparing Means

■ 25.5 ANOVA on Observational Data

26 Multifactor Analysis of Variance 78126.1 An ANOVA Model ■ 26.2 Assumptions and Conditions

■ 26.3 Inference When Effects of Variables Are Related

27 Multiple Regression 81427.1 Multiple Regression Motivation ■ 27.2 The Multiple Regression

Model ■ 27.3 Multiple Regression Inference ■ 27.4 Comparing

Multiple Regression Models

28 Multiple Regression Wisdom 84328.1 Indicators ■ 28.2 Diagnosing Regression Models:

Looking at the Cases ■ 28.3 The Best Multiple Regression Model

■ 28.4 Regression Roles ■ 28.5 Indicators for Three or More Levels

Part VII: Inference When Variables Are Related 880

Part VIII Inference By Other Means

29 Rank-Based Nonparametric Tests 89729.1 Wilcoxon Rank Sum Test ■ 29.2 Kruskal-Wallis Test ■ 29.3 Wilcoxon

Signed Rank Test for Paired Data ■ 29.4 Friedman Test for

a Randomized Block Design ■ 29.5 Rank Correlation

30 The Bootstrap 923

30 The Bootstrap (online only)30.1 The Basic Idea ■ 30.2 Bootstrapping the Sampling Distribution of

a Sample Mean ■ 30.3 Bootstrapping the Standard Error of the Statistic

■ 30.4 Confidence Intervals ■ 30.5 Bootstrapping the Median

■ 30.6 Bootstrap Assumptions and Conditions ■ 30.7 More Complicated

Data Structures

AppendixesA Answers A-1 ■ B Index B-1 ■ C Tables C-1

*Optional section.

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x

We are often asked why we write Statistics texts. After all, it takes a lot of work to find new and better examples, to keep datasets current, and to make a book an enjoyable and effective learning tool. So we thought we’d address that question first.

We do it because it’s fun. Of course, we care about teaching students to think statistically; we are teachers and

professional statisticians. But Statistics can be a particularly challenging subject to teach. The student encounters many new concepts, many new methods, and many new terms. And we want to change the way students think about the world. From the start, our chal-lenge has been to write a book that students would read, learn from, and enjoy. And we return to that goal with each new edition.

The book you hold is shorter, quicker to the point, and, we hope, even more read-able than the first Canadian edition. Of course, we’ve kept our conversational style and background anecdotes.1 But we’ve tightened discussions and adjusted the order of some topics to move the story we tell about Statistics even more quickly to interesting real-world questions. We’ve focused even more on statistical thinking.

More and more instructors are using examples from Statistics to provide intuitive examples of how a little bit of math can help us say a lot about the world. So students expect Statistics to be about real-world insights. This second Canadian edition of Stats: Data and Models keeps your students engaged and interested because we show Statistics in action right from the start. Students will be solving problems of the kind they’re likely to encounter in real life sooner. In Chapter 4, they will be comparing groups and in Chapter 6, they’ll see relationships between two quantitative variables—and, of course, always with real, modern data.

There are few things more fun and useful for students than being empowered to dis-cover something new about the world. And few things more fun for authors than helping students make those discoveries.

So, What’s New in the Second Canadian Edition?We’ve rewritten sections throughout the book to make them clearer and more interesting, along with many new up-to-the-minute motivating examples.

We’ve added some new features, each with the goal of making it even easier for students to put the concepts of Statistics together into a coherent whole.

1. New and improved pedagogical tools: A new section head list at the beginning of each chapter provides a road map. Section heads within each chapter are reorga-nized, numbered, and reworded to be clear and specific. Chapter study materials now include Learning Objectives as well as Terms. Students who understand the Objectives and know the Terms are well on their way to being ready for any tests.

2. Streamlined design: Our goal has always been an accessible text. This edition sports a new design that clarifies the purpose of each text element. Essential supporting material is clearly boxed and shaded, so students know where to focus their study efforts. Enriching—and often entertaining—side material is boxed, but not shaded.

3. Streamlined content: Our reorganization has shortened the book from 33 to 30 chapters. Each chapter is still a focused discussion, and most can be taught in one lesson. We’ve reduced time spent on secondary topics. The result is a more readable text.

Preface

1and our footnotes.


Preface xi

4. Content changes: Here’s how we’ve reorganized or changed the content:

a. Chapter 1 now gets down to business immediately rather than just providing an introduction to the book’s features.

b. The discussion of re-expression or transformation of data is no longer a sepa-rate chapter, but instead is dispersed throughout other chapters.

c. The discussions of probability and random variables are tighter—and a chapter shorter—but also with a clearer introduction to continuous distributions. The geometric distribution has been moved to the exercises.

d. We’ve added an optional section on Logistic Regression to the Inference for Regression chapter, an optional section “How ANOVA Works—the Gory Details” to our chapter on multi-factor ANOVA, and the test for a set of betas to the first chapter on multiple regression.

e. We’ve removed the 10% Condition when inference is about means.

5. Exercises: We’ve updated most exercises that use real-world data, retired some that were getting old, and added new exercises. As in the previous edition, many of the exercises feature Canadian content and data with up-to-date references to Aboriginal peoples, sports, health care, education, the environment, and other social and political issues. And we continue to add exercises requiring computer simulations.

6. Major sections have new numbers to help with navigation and reading assignments.

7. On the Computer sections now include instructions for R and StatCrunch.

Our ApproachStatistics is practiced with technology. We think a modern statistics text should recognize that fact from the start. And so do our students. You won’t find tedious calculations worked by hand. But you will find equation forms that favour intuition over calculation. You’ll find extensive use of real data—even large data sets. And, most important, you’ll find a focus on statistical thinking rather than calculation. The question that motivates each of our hundreds of examples is not “how do you find the answer?” but “how do you think about the answer?”

We’ve been guided in the choice and order of topics by several fundamental prin-ciples. First, we have tried to ensure that each new topic fits into the growing structure of understanding that we hope students will build. We know that learning a new subject like Statistics requires putting together many new ideas, and we have tried to make that process as natural as possible. That goal has led us to cover some topics in a different order than is found in other texts.

As one example, we introduce inference by looking at a confidence interval for a proportion rather than the more traditional approach that starts with the one-sample mean. Everyone has seen opinion polls. Most people understand that pollsters use a sample of voters to try to predict the preferences of the entire population and that the result is an esti-mate with a margin of error. Showing how to construct and interpret a confidence interval in this context introduces key concepts of inference in a familiar setting. We next examine hypothesis tests for a proportion. The mechanics use formulas and a test statistic that is now familiar, so it is easier to focus on the logic and structure of a formal hypothesis test. After hypothesis tests we examine inference about the difference between two propor-tions. We build on the understanding of confidence intervals and hypothesis tests as we introduce the added complexity of comparing two groups. When we then turn our atten-tion to the more complicated topic of small-sample inference for means, the only new issue is the t-distribution. However, should you wish to discuss the z-test and confidence interval for means (known sigma or large n) earlier, in tandem with the z-procedures for proportions, optional sections are included in those chapters.


xii Preface

Textbooks can be defined more by what they choose not to cover than by what they do cover. We’ve structured this text so that each new topic fits into a student’s growing understanding. Several topic orders can support this goal. While this text contains mate-rial sufficient for two semesters, it offers good choices for those teaching a one-semester course, for which we recommend coverage of Chapters 1–22, followed by (time permit-ting) one or more of Chapters 23, 24, 25, and 29. Some topics in Chapters 9–11 may be de-emphasized or omitted entirely depending on the needs of the particular audience. Likewise, the material on probability and random variables in chapters 12–14 is consid-erable and portions may be de-emphasized. Optional sections, indicated by asterisks, are indeed just that and may be omitted without harm.

GAISE GuidelinesThe Guidelines for Assessment and Instruction in Statistics Education (GAISE) report adopted by the American Statistical Association urges that Statistics education should

1. emphasize Statistical literacy and develop Statistical thinking,

2. use real data,

3. stress conceptual understanding rather than mere knowledge of procedures,

4. foster active learning,

5. use technology for developing concepts and analyzing data, and

6. make assessment a part of the learning process.

We’ve designed our text and supporting materials to support this approach to the introductory course. We urge you to think about these guidelines with each class meeting.

Our Goal: Read This Book! The best text in the world is of little value if students don’t read it. Here are some of the ways we have made this edition even more approachable:

• Readability. This book doesn’t read like other Statistics texts. Our style is both col-loquial and informative, engaging students to actually read the book to see what it says. We’ve tightened the discussions and eliminated digressions.

• Humour. We know that humour is the best way to promote learning. You will find quips and wry comments throughout the narrative, in margin notes, and in footnotes.

• Informality. Our informal diction doesn’t mean that we treat the subject matter lightly or informally. We try to be precise and, wherever possible, we offer deeper explanations and justifications than those found in most introductory texts.

• Focused lessons. The chapters are shorter than in most other texts so instructors and students can focus on one topic at a time.

• Consistency. We try to avoid the “do what we say, not what we do” trap. Having taught the importance of plotting data and checking assumptions and conditions, we model that behavior right through the rest of the book. (Check the exercises in Chapter 24. You’ll see that we still require and demonstrate the plots and checks that were introduced in the early chapters.) This consistency helps reinforce these fundamental principles.

• The need to read. Students who plan just to skim the book may find our presentation frustrating. The important concepts, definitions, and sample solutions don’t sit in little boxes. Statistics is a consistent story about how to understand the world when we have data. The story can’t be told piecemeal. This is a book that needs to be read, so we’ve tried to make the reading experience enjoyable.


Preface xiii

MathematicsMathematics can

1. provide a concise, clear statement of important concepts.

2. describe calculations to be performed with data.

3. embody proofs of fundamental results.

Of these, we emphasize the first. Mathematics can make discussions of Statistics con-cepts, probability, and inference clear and concise. We don’t shy away from using math where it can clarify without intimidating. But we know that some students are discour-aged by equations, so we always provide a verbal description and a numerical example as well. Some theorems about Statistics are quite interesting, and many are important. Often, though, their proofs are not enlightening to introductory Statistics students and can distract the audience from the concepts we want them to understand. So we avoid them here.

Nor do we slide in the opposite direction and concentrate on calculation. Although statistics calculations are generally straightforward, they are also usually tedious. And, more to the point, they are often unnecessary. Today, virtually all statistics are calculated with technology, so there is little need for students to spend time summing squared deviations by hand. We have selected the equations that do appear for their focus on illuminating concepts and methods. Although these equations may be the best way to understand the concepts, they may not be optimal for hand calculation. When that happens, we give an alternative formula, better suited for hand calculation, for those who find following the process a better way to learn about the result.

Technology and Data To experience the real world of Statistics, use modern technology to explore real data sets.

Technology. We assume that you are using some form of technology—a statistics package, a calculator, a spreadsheet, or some combination of these—in your Statistics course. We also assume that you’ll put little emphasis on calculating answers by hand, even though we often show how. However, this is not a technology-heavy book. The role of technology in this book is to get the calculations out of the way so we can focus on statistical thinking. We discuss generic computer output, but we don’t adopt any particular statistics software. We do offer guidance to help students get started on eight common software platforms: Excel®, Minitab®, JMP®, SPSS®, TI-83/84 Plus graphing calculators, StatCrunch®, and R®. The On the Computer section at the end of most chapters is specific to the methods learned in that chapter.

Data. Because we use technology for computing, we don’t limit ourselves to small, artificial data sets. You’ll find some small data sets, but we also base examples and exer-cises on real data with a moderate number of cases—usually more than you would want to enter by hand into a program or calculator. Machine-readable versions of the data are included on the book’s website, www.pearsoncanada.ca/deveaux.

Continuing Features

Enhancing UnderstandingWhere Are We Going? Each chapter starts with a paragraph that points out the kinds of questions students will learn how to answer in the chapter. A new chapter outline helps organize major topics for the students.


xiv Preface

Each chapter ends with a What Have We Learned? summary, which includes new learn-ing objectives and definitions of terms introduced in the chapter. Students can think of these as study guides.

In each chapter, our innovative What Can Go Wrong? sections highlight the most com-mon errors that people make and the misconceptions they have about Statistics. One of our goals is to arm students with the tools to detect statistical errors and to offer practice in debunking misuses of statistics, whether intentional or not.

Margin and in-text boxed notes. Throughout each chapter, boxed margin and in-text notes enhance and enrich the text. Boxes with essential or practical information are screened. Conversational notes that enhance the text and entertain the reader are unscreened.

Math Boxes. In many chapters we present the mathematical underpinnings of the statisti-cal methods and concepts. By setting these proofs, derivations, and justifications apart from the narrative, we allow the student to continue to follow the logical development of the topic at hand, yet also refer to the underlying mathematics for greater depth. Depending on the nature of the course, instructors can decide whether or not to assign these boxes to their students.

By Hand. Even though we encourage the use of technology to calculate statistical quanti-ties, we realize the pedagogical benefits of occasionally doing a calculation by hand. The By Hand boxes break apart the calculation of many simpler formulas to help the student through the calculation of a worked example.

Reality Check. We regularly remind students that Statistics is about understanding the world with data. Results that make no sense are probably wrong, no matter how carefully we think we did the calculations. Mistakes are often easy to spot with a little thought, so we ask students to stop for a reality check before interpreting their result.

Notation Alert. Throughout this book, we emphasize the importance of clear commu-nication, and proper notation is part of the vocabulary of Statistics. We’ve found that it helps students when we are clear about the letters and symbols statisticians use to mean very specific things, so we’ve included Notation Alerts whenever we introduce a special notation that students will see again.

Connections. Each chapter has a Connections feature to link key terms and concepts with previous discussions and to point out the continuing themes. Connections help students fit newly learned concepts into a growing understanding of Statistics.

Learning by ExampleFor Example. As we introduce each important concept, we provide a focused example applying it—usually with real up-to-the-minute data. Many For Examples carry the dis-cussion through the chapter, picking up the story and moving it forward as students learn more about the topic.

Step-by-Step Examples: Think, Show, Tell. Step-by-Step examples repeat the mantra of Think, Show, and Tell in every chapter. These longer, worked examples guide students through the process of analyzing the problem with the general explanation on the left and the worked-out problem on the right. They emphasize the importance of thinking about a Statistics question (What do we know? What do we hope to learn? Are the assumptions and conditions satisfied?) and reporting our findings (the Tell step). The Show step contains the mechanics of calculating results and conveys our belief that it is only one part of the process. The result is a better understanding of the concept, not just number crunching.

Testing UnderstandingJust Checking. Just Checking questions are quick checks throughout the chapter; most involve very little calculation. These questions encourage students to pause and think


Preface xv

about what they’ve just read. The Just Checking answers are at the end of the exercise sets in each chapter so students can easily check themselves.

Exercises. Exercises have been updated with the most recent data. Many come from news stories; some from recent research articles. Whenever possible, the data are available on MyStatLab and the Companion Website for the book so students can explore them further.

TechnologyActivStats Pointers. MyStatLab for this book includes ActivStats, so we’ve included occasional pointers to the ActivStats activities when they parallel discussions in the book. Many students choose to look at these first, before reading the chapter or attending a class on each subject.

Data Sources. Most of the data used in examples and exercises are from real-world sources, and whenever we can, we include references to the Internet data sources used, often in the form of URLs. The data we use are usually on MyStatLab and the Companion Website. If you seek the data—or an updated version of the data—on the Internet, we try to direct you to a good starting point.

On the Computer. In the real world, Statistics is practiced with computers. We prefer not to choose a particular Statistics program. Instead, at the end of most chapters, we summarize what students can find in the most common packages, often with anno-tated output. We then offer specific guidance for several of the most common packages (Excel®, JMP®, Minitab®, R®, SPSS®, StatCrunch®, and TI-83/84 Plus2) to help students get started with the software of their choice.

2For brevity, we will write TI-83/84 Plus for the TI-83 Plus and/or TI-84 Plus. Keystrokes and output remain the same for the TI-83 Plus and the TI-84 Plus, so instructions and examples serve for both calculators.


xvi

For InstructorsInstructor resources are password protected and available for download via the Pearson online catalogue at http:// catalogue.pearsoned.ca.

Instructor’s Solutions Manual. This resource provides com-plete, detailed, worked-out solutions for all the exercises in the textbook and is available through the online catalogue in both PDF and Word formats.

TestGen and Test Item File. For your convenience, our test-bank is available in two formats. TestGen is a computerized testbank containing a broad variety of multiple-choice, short answer, and more complex problem questions. Questions can be searched and identified by question type, level of difficulty, and skill type (computational or conceptual). Each question has been checked for accuracy and is available in the latest version of TestGen software. This software package allows instructors to custom design, save, and generate classroom tests. The test program permits instructors to edit, add, or delete questions from the test bank; edit existing graphics and create new ones; analyze test results; and organize a database of tests and student results. This software allows for greater flexibility and ease of use. It provides many options for organizing and displaying tests, along with search and sort features. The same questions can also be found in a Test Item File available in Word format. The TestGen testbank and Test Item File can be downloaded from the online catalogue.

PowerPoint® Presentations. These PowerPoint® lecture slides provide an outline to use in a lecture setting, presenting definitions, key concepts, and figures from the textbook. These lecture slides can be downloaded from the online catalogue.

Clicker Questions. Clicker questions provide in-the-moment opportunities to engage and to assess students’ understanding during lecture. Clicker Questions in PowerPoint® format can be downloaded from the online catalogue.

Image Library. The Image Library provides access to many of the images, figures, and tables in the textbook and is avail-able to instructors on the online catalogue.

Pearson Custom LibraryFor enrollments of at least 25 students, you can create your own textbook by choosing the chapters that best suit your own course needs. To begin building your custom text, visit www.pear-soncustomlibrary.com. You may also work with a dedicated Pearson Custom Editor to create your ideal text—publishing your own original content or mixing and matching Pearson con-tent. Contact your local Pearson representative to get started.

For StudentsStudent Solutions Manual. This solutions manual provides complete worked-out solutions to all of the odd-numbered

exercises in the book, expanding on the answers provided in the Appendix at the back of the text. (ISBN-13: 978-0-321-99162-1).

CourseSmart for Students. CourseSmart goes beyond tra-ditional expectations—providing instant, online access to the textbooks and course materials you need at an average sav-ings of 60%. With instant access from any computer and the ability to search your text, you’ll find the content you need quickly, no matter where you are. And with online tools like highlighting and note-taking, you can save time and study efficiently. See all the benefits at www.coursesmart.com/students.

Companion Web Site. The Companion Web Site provides additional resources (and data files) for instructors and students: www.pearsoncanada.ca/deveaux.

Technology ResourcesMyStatLab™ Online Course (access code required)MyStatLab is a course management system that delivers proven results in helping individual students succeed.

• MyStatLab can be successfully implemented in any environment—lab-based, hybrid, fully online, traditional— and demonstrates the quantifiable difference that inte-grated usage has on student retention, subsequent success, and overall achievement.

• MyStatLab’s comprehensive online gradebook auto-matically tracks students’ results on tests, quizzes, homework, and in the study plan. Instructors can use the gradebook to provide positive feedback or inter-vene if students have trouble. Gradebook data can be easily exported to a variety of spreadsheet programs, such as Microsoft Excel. You can determine which points of data you want to export, and then analyze the results to determine success.

MyStatLab provides engaging experiences that personalize, stimulate, and measure learning for each student. In addition to the following resources, each course includes a full inter-active online version of the accompanying textbook.

• Tutorial Exercises with Multimedia Learning Aids: The homework and practice exercises in MyStatLab align with the exercises in the textbook, and they regenerate algorithmically to give students unlimited opportunity for practice and mastery. Exercises offer immediate helpful feedback, guided solutions, sample problems, animations, videos, and eText clips for extra help at point-of-use.

• StatTalk Videos: 24 Conceptual Videos to Help You Actually Understand Statistics. Fun-loving statistician Andrew Vickers takes to the streets of Brooklyn, New York, to demonstrate important statistical concepts through

Supplements


Supplements xvii

interesting stories and real-life events. These fun and engaging videos will help students actually understand statistical concepts. Available with an instructor’s user guide and assessment questions.

• Getting Ready for Statistics: A library of questions now appears within each MyStatLab to offer the devel-opmental math topics students need for the course. These can be assigned as a prerequisite to other assign-ments, if desired.

• Conceptual Question Library: In addition to algo-rithmically regenerated questions that are aligned with your textbook, there is a library of 1000 Conceptual Questions available in the assessment manager that requires students to apply their statistical understanding.

• StatCrunch®: MyStatLab integrates the web-based sta-tistical software, StatCrunch, within the online assess-ment platform so that students can easily analyze data sets from exercises and the text. In addition, MyStatLab includes access to www.StatCrunch.com, a website where users can access more than 15,000 shared data sets, conduct online surveys, perform complex analyses using the powerful statistical software, and generate compelling reports.

• Statistical Software Support: Knowing that students often use external statistical software, we make it easy to copy our data sets, both from the ebook and the MyStatLab questions, into software such as StatCrunch, Minitab, Excel, and more. Students have access to a variety of support tools—Technology Tutorial Videos, Technology Study Cards, and Technology Manuals for select titles—to learn how to effectively use statistical software.

And, MyStatLab comes from a trusted partner with educa-tional expertise and an eye on the future.

• Knowing that you are using a Pearson product means know-ing that you are using quality content. That means that our eTexts are accurate and our assessment tools work. Whether you are just getting started with MyStatLab, or have a ques-tion along the way, we’re here to help you learn about our technologies and how to incorporate them into your course.

To learn more about how MyStatLab combines proven learning applications with powerful assessment, visit www.mystatlab .com or contact your Pearson representative.

MathXL® for Statistics Online Course (access code required)MathXL® is the homework and assessment engine that runs MyStatLab. (MyStatLab is MathXL plus a learning manage-ment system.)

With MathXL for Statistics, instructors can:

• Create, edit, and assign online homework and tests using algorithmically generated exercises correlated at the objective level to the textbook.

• Create and assign their own online exercises and import TestGen tests for added flexibility.

• Maintain records of all student work, tracked in MathXL’s online gradebook.

With MathXL for Statistics, students can:

• Take chapter tests in MathXL and receive personalized study plans and/or personalized homework assignments based on their test results.

• Use the study plan and/or the homework to link directly to tutorial exercises for the objectives they need to study.

• Students can also access supplemental animations and video clips directly from selected exercises.

• Knowing that students often use external statistical soft-ware, we make it easy to copy our data sets, both from the eText and the MyStatLab questions, into software like StatCrunch™, Minitab, Excel, and more.

MathXL for Statistics is available to qualified adopters. For more information, visit our website at www.mathxl.com, or contact your Pearson representative.

StatCrunch®

StatCrunch is powerful web-based statistical software that allows users to perform complex analyses, share data sets, and generate compelling reports of their data. The vibrant online community offers more than 15,000 data sets for students to analyze.

• Collect. Users can upload their own data to StatCrunch or search a large library of publicly shared data sets, spanning almost any topic of interest. Also, an online survey tool allows users to quickly collect data via web-based surveys.

• Crunch. A full range of numerical and graphical meth-ods allow users to analyze and gain insights from any data set. Interactive graphics help users understand sta-tistical concepts, and are available for export to enrich reports with visual representations of data.

• Communicate. Reporting options help users create a wide variety of visually-appealing representations of their data.

Full access to StatCrunch is available with MyStatLab, and StatCrunch is available by itself to qualified adopters. StatCrunch Mobile is now available to access from your mobile device. For more information, visit our website at www.StatCrunch.com, or contact your Pearson representative.


xviii

A very big thanks goes to Shivon Sue-Chee for her hard work on the exercise sections, data sets, and appendix solutions. Her hard work and conscientiousness were a marvel. And what a huge contribution was made by Craig Burkett. Going beyond the call of duty far too often, his substantial contributions to the text made this edition possible. Also, thanks to Aaron Springford and Andy Leung for their help in updating the Instructor’s Manual. They often went above and beyond in their efforts to help us correct not only the manual, but the text itself.

Thank you also to the team at Pearson and EPS who supported the revision of this text throughout the writing and production process including John Polanszky, Senior Developmental Editor; Marissa Lok, Project Manager; Patricia Cardullo, Program Manager; Michelle Bish, Marketing Manager; Anthony Leung, Designer; Kayci Wyatt, Content Project Manager, Electronic Publishing Services; Janice Dyer, copy editor; Rachelle Redford, proofreader; and Eric Smith and Jingjing Wu, tech checkers.

Finally, thank you to the reviewers who provided feedback on our work and helped guide our plans for this new edition, including:

Julius Bankole, College of New Caledonia Ken Butler, University of Toronto at Scarborough Henry Kolacz, University of Alberta Dot Miners, Brock UniversityAllyson Rozell, Kwantlen Polytechnic University Gary Sneddon, Mount Saint Vincent University Asokan Mulayath Variyath, Memorial University of Newfoundland

Acknowledgments


xix

Index of Application

AgricultureAmerican Angus Association (FE) Ch. 20,

p. 564BST (E) Ch 22, p. 656Cereal (E) Ch 9, p. 273Cloud seeding (E) Ch 4, p. 107Ephedra (E) Ch 10, p. 299Fertilizers (E) Ch 25, p. 776–777Genetically modified corn (JC) Ch. 13,

p. 363Grass species (E) Ch 2, p. 37Peas (E) Ch. 26, p. 812Pesticides (E) Part VII Review, p. 892Seeds (E) Ch 15, p. 444Sprouts (E) Ch. 26, p. 807, 809Tiny black potato flea beetles (JC) Ch. 23,

p. 670–671Tomatoes (E) Ch 5 p. 143, (E) Ch 11 p. 326Vineyards (E) Ch 1 p. 12 (E) Ch 4

p. 104 (E) Part II Review 254 (E) Part III Review 334

Arts and LiteratureBarbershop music (E) Part VII Review,

p. 885–886Lefties and music (E) Part VII Review,

p. 888Mozart (E) Ch 11, p. 326Music and memory (E) Ch 21 p. 631 (E)

Part I Review p. 154Music library (E) Ch, p. 143Music students (E) Ch. 29, p. 918Pottery (E) Ch 6, p. 179–180 (E) Part VI

Review, p. 691–692Rap (E) Ch 21, p. 631Rock concert accidents (E) Ch 3, p. 85Singers (E) Ch 3, (E) Part I Review p. 147,

p. 76

BiologyBeetles (E) Ch 11, p. 328Bird counts (E) Ch 3, p. 78Cattle (E) Ch 5, p. 139, p. 141Crocodile (E) Part II Review, p. 258Cuckoos (E) Ch 21, (E) Ch. 29 p. 921,

p. 631Eggs (E) Ch 5 p. 143 (E) Ch 14 p. 406Elephants and hippos (E) Ch 8, p. 244–245Feeding fish (E) Part VI Review, p. 693

Fish (E) Ch 10, p. 300Fruit flies (E) Ch 23, p. 681Gators (E) Ch 7, p. 218Irises (E) Part VI Review, p. 698Manatees (E) Part II Review, p. 255Weighing bears (E) Ch 1, p. 11

BusinessAbsenteeism (E) Ch 13, p. 374Accounting (E) Ch 10, p. 300Ad campaign (E) Ch 21, p. 627Assembly (E) Part I Review, p. 156–157Assets (E) Ch 4, p. 113Bank tellers (E) Ch 14, p. 409CEO (E) Ch 15, p. 445–446Credit card charges (E) Ch 20, p. 591Dairy sales (E) Ch. 28, p. 892Fire insurance (E) Ch 12, p. 350Fraud detection (E) Part I Review,

p. 148–149Frumpies (E) Ch 11, p. 327Hiring (E) Ch 16, p. 474Industrial experiment (E) Ch 4, p. 108Industry codes (E) Ch 3, p. 79–80Insurance (E) Ch 14, (E) Part IV Review,

p. 405, p. 415Interest rates (E) Ch 8, p. 242–243Labour force (E) Part VI Review, p. 697Loans (E) Ch 18, p. 527Marketing (E) Ch 15, p. 441Mutual funds (E) Ch. 28, p. 882Online insurance (E) Ch 22, p. 650–651Pay (E) Part I Review, p. 155Payroll (E) Ch 3, p. 76 (E) Ch 5, p. 138Profits (E) Part II Review, p. 262, (E) Part I

Review, p. 157Real estate (E) Ch 7, p. 211Receptionist performance (E) Ch. 27,

p. 838–839Sales and Profits (E) Ch 24, p. 736,

p. 737Sex sells (E) Ch 22, p. 649, p. 654Sick days (E) Ch 3, p. 75–76Software (E) Ch 14, p. 404Stock market (E) Ch 14, p. 404Stocks, (E) Part IV Review, p. 415Tips (E) Ch 11 p. 322–323, (E) Part III

Review, p. 332–333TV ads (E) Ch 17, p. 499Workers, (E) Part IV Review, p. 413

EconomicsCost of living (E) Ch 7, p. 215Family income (E) Ch 3, p. 84Fuel economy (E) Ch 1, p. 12 (E) Ch 6,

p. 181–182 (E) Ch 10, p. 300High Net Worth Individuals (HNWI) (E)

Part I Review, p. 152House prices (E) Ch 3, p. 78Human Development Index (E) Ch 6,

p. 186, p. 187Math and gender (E) Ch 7, p. 219–220Money and degrees (E) Ch 7, p. 211Prices comparison (FE) Ch. 6, p. 161, p. 168

EducationArt students (E) Ch. 29, p. 918Bernoulli trials (E) Ch 14, p. 407Census at school (E) Ch 6, p. 185 (E) Ch 8

p. 250College (E) Part II Review, p. 253–254CPMP (E) Ch 21, p. 624, p. 626Cramming (E) Part I Review p. 149 (E) Part

II Review p. 256 (E) Part VII Review p. 895

Dollars (E) Part II Review, p. 260Education by age (E) Ch 23, p. 687Family planning (E) Part VI Review, p. 700Final exams (E) Ch 5, (E) Ch. 27 p. 836–838,

p. 138French vocabulary test (E) Part II Review,

p. 260Freshman (E) Ch 22, p. 649, p. 655Gender and school (E) Part VI Review,

p. 695–696Geography (E) Ch 9, p. 272–273GPA and SATs (E) Ch. 27, p. 839Grades (E) Ch 23, p. 686, Ch 24, 739 (E)

Ch 8 p. 242 (E) Part II Review, p. 256 (E) Ch. 27, p. 840–841, Part VII Review, p. 918

Graduate admissions (E) Ch 2, p. 42Graduation (E) Ch 4, p. 103–104Height and reading (E) Ch 6, p. 183Homecoming (E) Part III Review, p. 333Internet-based TOEFL test (iBT) (SBS)

Ch. 5, p. 120–121, p. 128–129Kindergarten (E) Ch 5, p. 142Learning math (E) Ch 21, p. 624LSAT scores (E) Ch 11, p. 328Lunchtime (E) Part II Review, p. 261

Note: (E) 5 Exercise, (FE) 5 For Example, (JC) 5 Just Checking, (IE) 5 in-text example (SBS) 5 Step-by-Step


xx Index of Application

Math and Gender (E) Ch 22, p. 654Math learning (E) Part VII Review,

p. 891–892Math scores (E) Ch 3, p. 80–81 (E) Ch 4,

p. 106–108 (E) Ch 8, p. 247–248

Misinterpretations (E) Ch 7, p. 212Online testing (E) Part VI Review,

p. 627–698Ontario students (E) Ch 21, p. 629 (E)

Ch. 29, p. 921PISA reading scale (E) Part VI Review,

p. 697Placement exams (E) Ch 5, p. 138Professors (E) Ch 5, p. 139Race and education (E) Ch 23, p. 687Ranking university (E) Ch 23, p. 687–688Reading (E) Ch 4, p. 106 (E) Ch 8,

p. 241 (E) Ch 11 p. 328 (E) Ch 21, p. 626Recruiting (E) Part VI Review, p. 700SAT or ACT (E) Ch 5, p. 138 (E) Ch 7,

p. 213Schools (E) Ch 1, p. 11Statistics journals (E) Ch 21, p. 630–631Study habits (E) Part VII Review, p. 886Summer school (E) Ch 21, p. 627, Ch 22,

p. 652–653Teach for America (E) Part VI Review,

p. 697Test scores (E) Ch 3 p. 74–75, p. 84–85 (E)

Ch 4 p. 105TOEFL score (E) Ch 5, p. 141–143Togetherness (E) Part VII Review, p. 887Toronto students (E) Part I Review, p. 155

EntertainmentBig screen TVs (E) Part II Review, p. 259Climate change (E) Ch_28, p. 873Computer gaming (E) Ch 19, p. 553Gambling (E) Ch 12, (E) Ch 16, p. 352,

p. 473Hurricane predictions (E) Ch 24, p. 730–731Movie budgets (E) Ch 24, p. 731, p.732 (E)

Ch. 30 p. 946Movie dramas (E) Ch 8, p. 239–240Movies (E) Ch 12 p. 351 (E) Ch 13, p. 373MTV (E) Ch 22, p. 649Roller coasters (E) Ch 6 p. 180, p. 184 (E)

Ch 10 p. 298, p. 299

EnvironmentAcid rain (E) Part I Review p. 148 (E) Part

II Review p. 255 (E) Ch 24 p. 736Biomass of trees (E) Ch 1, p. 11Blackjack (E) Ch 5, p. 143Climate change (E) Ch 7, p. 216

Cold (E) Ch 5, p. 138, (E) Ch 12, p. 350Craters (FE) Ch. 24, p. 705–706, p. 719Earthquake in Japan (IE) Ch 3, p. 44Forest fire (E) Ch 2, p. 33, (E) Part VI

Review, p. 694–695Global warming (E) Ch 2, p. 34, Ch. 27,

p. 825Hard water (E) Part I Review, p. 150 (E)

Ch 6 p. 183 (E) Ch 7, p. 218 Ch 21 p. 627, p. 631 (E) Part VII Review p. 882

Heating (E) Ch 8, p. 242Hopkins Memorial Forest (E) Ch 4, p. 87Hurricanes (E) Ch 3, p. 77 (E) Ch 4, p. 112

(E) (FE) Ch. 7, p. 191, p. 197 Ch 8, p. 240 (E) Ch 21 p. 625–626

Hurricane frequencies (E) Ch 23, p. 682Hurricane Katrina (FE) Ch. 7, p. 197Nuclear power (E) Part VII Review,

p. 884–885Oil spills (E) Ch 2, p. 34Old Faithful (E) Part II Review, p. 258 (E)

Part VII Review, p. 887–888Ozone (E) Ch 24, p. 736, p. 737Pollution (E) Ch 15, p. 448Rain (E) Ch 12, (E) Ch 22 p. 649, p. 349Rainfall (E) Ch 4 p. 113 (E) Ch 9 p. 275 (E)

Part VII Review p. 894Rainmakers (E) Part VI Review, p. 699Seasons (E) Part I Review, p. 151Snow (E) Ch 12, p. 349Streams (E) Ch 21, p. 626, (E) Part VI

Review, p. 697Temperatures (E) Ch 5, p. 138, (E) Ch 22,

p. 651Tornadoes 2011 (E) Ch 3, p. 75Trees (E) Ch 5, p. 139Tsunamis (IE) Ch 5, p. 144Volcanoes (E) Part IV Review, p. 417Weather forecasts (E) Ch 2, p. 37Wind speed (FE) Ch. 8, p. 231 (E) Ch 22,

p. 650, p. 651Winter (E) Ch 12, p. 349

FinanceCredit card payment (JC) Ch. 17,

p. 488–489, p. 501Investments (E) Ch 1, p. 11Popcorn (E) Ch 20, p. 594Tracking sales (E) Ch 1, p. 11

Food and DrinksApples (E) Ch 14, (E) Ch 15 p. 444, p. 408Bread (E) Part I Review, p. 148, (E) Part VI

Review, p. 698Breakfast cereals (E) Ch. 27, p. 839–840Burger King (E) Ch. 27, p. 842

Burgers (E) Ch 6, p. 182 (E) Ch 7, p. 215Candy (E) Ch 7, p. 216Cereal (E) Ch 21, p. 625, (E) Ch 24, p. 735Chicken (E) Ch 7, p. 215Chocolate (E) Ch. 30, p. 944Coffee sales (E) Ch 6, p. 180Cranberry juice (E) Ch 23, p. 685Dim sum (E) Ch 2, p. 41–42Eating in front of the TV (E) Ch 23, p. 686Eggs (E) Ch 22, p. 649Groceries (E) Ch 9, p. 274Hams (E) Ch 5, p. 138Hot dogs (E) Ch 21, p. 623, Ch 24, p. 732Hungry (E) Ch 21, p. 628Lay’s (E) Part VI Review, p. 699M&M’s (E) Ch 23, p. 681Meals (E) Ch 15, p. 445, (E) Part VI

Review, p. 695Milk (E) Ch 15, p. 449Nuts (E) Ch 23, p. 681Orange juice (E) Ch 11, p. 332Pepsi (E) Ch 13, p. 372Pizza (E) Ch. 28, p. 873Popcorn (E) Ch. 26, p. 806, Ch. 29, p. 919,

Ch. 30, p. 944Popsicle sticks (E) Ch. 27, p. 842Quality control (E) Ch 10, p. 300Seafood (E) Ch 16, p. 472Sip size (E) Ch 3, p. 78Soup (E) Ch 15, p. 442Thirsty (E) Ch 21, p. 628Tim Hortons doughnuts (E) Ch 1, p. 11Tomatoes (E) Ch 11, p. 322–323Veggie burgers (E) Ch 7, p. 214–215Wine (E) Ch 11, p. 327, (E) Ch 4, p. 105Yogourt (E) Ch 22, p. 653, (E) Ch 25, p. 775

GamesBoard games (E) Ch 9, p. 275Cards (E) Ch 9, p. 273,(E) Ch 13, p. 372,

(E) Ch. 29, p. 921Casino (E) Ch 9, p. 272, (E) Ch 12 p. 349

(E) Ch 14 p. 407Coin tosses (E) Ch 9, p. 272Craps (E) Ch 13, p. 372Dice (FE) Ch. 9, p. 266–267 (E) Ch 9 p. 275

(E) Ch 13 p. 371 (E) Ch 17 p. 496, (E) Ch 23, p. 680–681

Lottery (E) Ch 9 p. 272, p. 273, p. 274 (E) Part III Review, p. 332 (E) Ch 23, p. 682

Music games (E) Ch 9, p. 274Pinball (E) Part VII Review, p. 892Quiz (E) Ch 9, p. 273Red cards (E) Ch 12, p. 352Spinner (E) Ch 12, p. 350Video racing (E) Part VII Review, p. 884


Index of Application xxi

GeneticsBrain plaque (E) Part III Review, p. 331Smoking gene (E) Ch 2, p. 38–39

HealthAcupuncture (E) Part III Review, p. 335Analgesics (E) Ch. 29, p. 918Anorexia (E) Ch 19, p. 551, (E) Ch 2, p. 38Antacids (E) Part III Review, p. 333–334Antidepressants (E) Ch 6, p. 181, (E)

Ch. 29, p. 920Antidepressants and bone fractures (E)

Ch 2, p. 39Arm length (E) Ch 10, p. 299–300Aspirin (JC) Ch. 17, p. 481Autism (E) Part IV Review, p. 414Avandia® (FE) Ch. 18, p. 503, p. 508,

p. 515, p. 516Babies (E) Ch 1, p. 11 (E) Ch 5, p. 142, (E)

Part VI Review, p. 693Baldness and heart disease (E) Ch 6, p. 184,

(E) Ch. 26, p. 808British Columbia birth weights (E) Part VI

Review, p. 691Bipolar disorder (E) Ch 11, p. 324 (E)

Part VI Review, p. 697Birth defects (E) Ch 9, p. 272Birth order (E) Part I Review, p. 154Birthrates (E) Ch 7, p. 216–217Blindness (E) Ch 1, p. 11Blood donors (E) Ch 9, p. 274Blood pressure (E) Ch 2 p. 40 (E) Ch 4

p. 104 (FE) Ch. 6, p. 166–167 (E) Ch 11 p. 324, (JC) Ch. 30, p. 925, p. 930

Blood proteins (E) Ch 2, p. 39Blood type (E) Ch 23, p. 681Body fat (E) Ch 7, p. 217 (E) Ch 24, p. 739

Ch. 27, p. 839, p. 840Body mass index (BMI) (FE) Ch. 15, p. 425Body temperatures (E) Ch 5, p. 142Breast cancer (E) Ch 11, p. 324Caffeine (E) Ch 4, p. 105Canadian Drug Use and Monitoring Survey

(CADMUS) (JC) Ch. 19, p. 543, p. 555

Canadian healthcare system (JC) Ch. 16, p. 454, p. 477

Carpal tunnel (E) Ch 19, p. 550 (JC) Ch. 21, p. 607, p. 610, p. 632

Childbirth (E) Ch 23, p. 682, p. 683Cholesterol (E) Ch 24, p. 732,Ch 4 p. 108

(E) Ch 5 p. 141Cigarettes (E) Ch 7, p. 211–212Colour blindnesss (E) Ch 9, p. 272 (E) Part

II Review, p. 258, (E) Part V Review, p. 557

Crohn’s disease (E) Part V Review, p. 560Diabetes and weight loss (E) Ch 22, p. 649Dialysis (E) Part I Review, p. 148Diet (E) Ch 11, p. 324,(E) Part VI Review,

p. 691Drug abuse (E) Ch 6, p. 182 (E) Ch 7,

p. 214Drug allergy test (JC) Ch. 17, p. 481Drug tests (E) Ch 9, p. 297Drugs (E) Part VI Review, p. 691Extreme values (E) Ch. 29, p. 918Eye and hair colour (E) Ch 4, p. 112Fish diet (E) Ch 23, p. 685Food safety (FE) Ch. 11, p. 312, p. 314,

p. 317Framingham heart study (E) Ch 4, p. 107Gastric freezing (JC) Ch. 11, p. 309–310,

p. 315, p. 329Genetics (E) Part VI Review, p. 692Gestation (E) Ch 8, p. 244 (JC) Ch. 15,

p. 437 (E) Ch. 29, p. 920Hand clinics (FE) Ch. 25, p. 745, p. 750Healing (E) Ch 11, p. 327–328Heart attacks and height (E) Ch 11, p. 323Herbal cancer (E) Part V Review, p. 557Herbal medicine (E) Ch 1, p. 12 (E) Part I

Review, p. 154HIV (E) Ch 14, p. 409HIV test (SBS) Ch. 13, p. 366Hospitals (E) Ch 2, p. 42Hospital stays (E) Ch 4, p. 104Infliximab (E) Part VII Review, p. 894Insomnia (E) Ch 11, p. 325–326Insulin and diet (E) Part VI Review,

p. 698–699Kidney failure, in cats and dogs (FE)

Ch. 11, p. 303, p. 304–305Legionnaires’ disease (E) Part VI Review,

p. 697Life expectancy (E) Ch. 29, p. 920Lou Gehrig’s disease (ALS) (E) Part VI

Review, p. 692Menopause (E) Ch 11, p. 323Mouth volume (JC) Ch. 24, p. 712–713Multiple sclerosis and vitamin D (E) Ch 11,

p. 323Neonatal brain (IE) Ch 6, p. 158New England Journal of Medicine (NEJM)

(FE) Ch. 18, p. 503Obesity (E) Ch 2, p. 37–38Omega (E) Ch 11, p. 325Preemies (E) Part VII Review, p. 888Pregnancy (E) Ch 16, p. 474, (E) Ch 23,

p. 687,(E) Part VII Review, p. 886Prenatal care (E) Part I Review, p. 147Prostate cancer (E) Ch 19, p. 550Prostate and fish (E) Ch. 26, p. 808

Pulse rates (E) Ch 21, p. 625Quebec abortions (E) Part II Review, p. 257Renters weight (E) Part VI Review, p. 693Rickets (E) Ch 14, p. 409Self-reported BMI (E) Ch 2, p. 40Shingles (E) Ch 11, p. 328Smoking (E) Ch 8 p. 238, p. 240 (E) Part VI

Review, p. 694Smoking and pregnancy (E) Part II Review,

p. 259Sugar in cereals (E) Ch 3 p. 74 (E) Ch 4

p. 105Systolic blood pressure (FE) Ch. 30,

p. 928–930Tablets evaluation by Consumer Reports

(magazine) (FE) Ch. 1, p. 4, p. 7Teen drinking (E) Part VI Review, p. 692Teen smokers (E) Ch 2, p. 33Weight loss (E) Part VII Review, p. 895Zinc content in cancer patients (E) Part VI

Review, p. 700

LawCrime comparisons (E) Ch 4,

p. 109–110Homicides (E) Ch 4, p. 111 (E) Part I

Review, p. 147 (E) Part VI Review, p. 693

Incarceration rates (E) Ch 3, p. 81Résumé fraud (E) Part VII Review, p. 884

MediaAd recall (E) Ch 21, p. 628Net-newsers (E) Part V Review, p. 558News (E) Ch 1, p. 10 (E) Ch 2, p. 33 (E)

Ch 3, p. 74 (E) Ch 4, p. 103Newspapers (E) Part VI Review, p. 695Online social networking (E) Ch 19, p. 549Science News (E) Ch 11, p. 324, (E) Ch 19,

p. 549WebZine (E) Ch 17, p. 498

MiscellaneousAmerican Association for Public Opinion

Research (AAPOR) (JC) Ch. 13, p. 360–361

Beanstalks (E) Part I Review, p. 148Batteries (E) Ch 14, p. 405 (E) Part VI

Review, p. 699–700Churches (E) Ch 10, p. 298Containers (E) Ch. 26, p. 809, p. 812Ethanol (E) Ch 12, p. 350Flight cancellations (SBS) Ch. 3, p. 63–64Hamsters (E) Part VI Review, p. 700Helmet sizes (E) Ch 5, p. 142


xxii Index of Application

House Prices (E) Ch 24, p. 731–732 (E) Ch. 27, p. 836–838

Lottery (JC) Ch. 12, p. 339, p. 352Music players (E) Ch 5, p. 138Orientation (E) Ch 1, p. 10–11Paper airplane design (JC) Ch. 18, p. 795,

p. 813Pet food (FE) Ch. 11, p. 306Pets (E) Ch 13, p. 372Pew Research Center (JC) Ch. 14, p. 393Real estate (FE) Ch. 27, p. 815–816 (FE)

Ch. 28, p. 851Refrigerators (E) Ch 1, p. 12Statue of Liberty’s nose (E) Part I Review,

p. 152Stereo systems (SBS) Ch. 14, p. 395–397Strategic Counsel for CTV (FE) Ch. 16,

p. 455, p. 456, p. 457Tableware (E) Part VII Review, p. 882Tattoos (E) Ch 2, p. 36–37Titanic (IE) Ch 2 p. 14 (E) Ch 13 p. 370 (E)

Ch 23, p. 683–684, p. 687Typing (E) Part VII Review, p. 889–890Washers (E) Ch 21, p. 623Washing (E) Ch. 26, p. 809, Ch. 29,

p. 918–919, Ch. 30, p. 945Water tank drains (E) Part II Review, p. 259Wild horses (E) Part VII Review,

p. 883–884Women researchers (E) Ch 4, p. 109Zip codes (E) Ch 3, p. 79

PeopleAboriginals (E) Part II Review, p. 256–257Age of couples (E) Ch 8, p. 243–244Age (E) Part VI Review, p. 694Asian immigrants (E) Ch 8, p. 251Bilingual population (E) Ch 3,

p. 82–83Births (E) Part VII Review, p. 891Body-size measurements (E) Ch 6, p. 185Egyptians (E) Ch 21, p. 625, (E) Ch. 29,

p. 921Egyptian skulls (E) Ch. 29, p. 945Emigration (E) Ch 12, p. 351Eye and hair colour (E) Part VII Review,

p. 893First Nations Band sizes (E) Ch 3, p. 81,

p.83–84 (E) Ch 4 p. 114 (E) Part I Review p. 149–150

Gender gap (E) Ch 8, p. 246–247Hearing (E) Part VI Review, p. 694Immigrant commuters (E) Ch 7, p. 219Immigration (E) Ch 12, p. 351Interprovincial migration (E) Ch 6,

p. 185–186 (E) Ch 8 p. 247

Life expectancy (E) Ch 8, p. 245–246Mandarin or Cantonese (E) Ch 6, p. 186 (E)

Ch 8, p. 248–250Marriage age (E) Ch 7, p. 239 (E) Ch 24,

p. 732–733Neighbourhood density (E) Ch 2, p. 38Ontario interprovincial migration (E) Ch 7,

p. 218–219Ontario migration (E) Ch 6, p. 185Pet ownership (E) Ch 2, p. 39Population growth (E) Ch 4, p. 106 Part I

Review, p. 150South American immigrants (E) Ch 8,

p. 245Twins (E) Ch 2, p. 39–40 (E) Part VI

Review, p. 693, p. 694

Politics2000 Election and spoiled ballots (E) Ch 4,

p. 1102006 Election (IE) Ch 4, p. 1112011 Election (IE) Ch 3, p. 75 (E) Ch 4,

p. 110–111 (E) Ch 9, p. 2732011 Maritimes Election (IE) Ch 3,

p. 81–82, p.84Bingeing and Politics (IE) Ch 23,

p. 684–685Fifty states (IE) Ch. 27, p. 841, Ch. 28,

p. 874Negative countries (IE) Ch 2, p. 34Opinion polling organizations (JC) Ch. 13,

p. 357–358Political views (IE) Ch 2, p. 36Spoiled ballots (JC) Ch. 4, p. 91–92, p. 114Students and politics (IE) Ch 2, p. 36

PsychologyAntisocial behaviors (JC) Ch. 2, p. 24, p. 43Birth order (E) Ch 23, p. 684Brain size (E) Ch 24, p. 735Brain waves (E) Ch 22, p. 652Colour or Text (E) Part VI Review, p. 699Crawling (E) Ch 24, p. 737–738 (E) Part VI

Review, p. 691Depression and the Internet (E) Part VII

Review, p. 891Depression (E) Ch 19, p. 551 (E) Part II

Review, p. 260Effect of the full moon (E) Ch 23, p. 686Honesty (E) Ch 1 p. 11 (E) Ch 11 p. 323IQ scores versus brain size (E) Ch 6, p. 179Mazes and smells (E) Part VI Review,

p. 691Memory (E) Ch 21, p. 624Neurological research (E) Part IV Review,

p. 414

Optimism (E) Ch 12, p. 371Sex and violence (E) Ch 21, p. 627–628Social life (E) Ch 10, p. 298Stereograms (E) Ch 4, p. 113–114 (E)

Ch 21, p. 624Walking in circles (E) Ch 1, p. 12

ScienceAcid rain (E) Ch 3, p. 78Chromatography (E) Ch. 26, p. 810, p. 811Engines (E) Part I Review, p. 154–155Flowers (E) Ch 1, p. 11–12Glycogen (E) Ch 11, p. 325Molten iron (E) Ch 1, p. 11Planet distances and years (E) Ch 8, p. 251Planets (E) Ch 6, p. 186Streams (E) Ch 1, p. 12 (E) Part I Review,

p. 12

Social IssuesAboriginal identity (E) Ch 2, p. 41Aboriginal languages (E) Ch 2, p. 35Canadian languages (E) Ch 2, p. 36Census (E) Ch 12, p. 350Driver deaths (E) Part VI Review, p. 693Pubs (E) Part III Review, p. 334Racial steering (E) Ch 23, p. 687Smoking (E) Ch 8 p. 238, p. 240 (E) Part VI

Review, p. 694Teen drinking (E) Part VI Review, p. 692Teen traffic deaths (E) Ch. 28, p. 888–889Violence against women (E) Ch 23, p. 681

SportsAmerican League baseball games (E) Ch 6,

p. 182Athletics (E) Part VII Review, p. 894–895Baseball (E) Ch 21, p. 626–627Basketball (E) Ch 7, p. 274 (E) Ch. 26,

p. 808Bowling (E) Part VII Review, p. 884Cryotherapy for athletes (E) Ch 2, p. 35Curling (E) Part I Review, p. 156Football (E) Part VII Review, p. 882Golf (E) Ch 21, p. 630, p. 631Golf courses (E) Ch 3, p. 80Hamstrings (E) Ch 11, p. 326Heptathlon (E) Ch 24, p. 735–736 (E) Ch 7,

p. 217Hockey (E) Ch 17, p. 498Hockey players (FE) Ch. 23, p. 659, p. 660Javelin (E) Part VII Review, p. 893Jerseys (E) Part IV Review, p. 416Jumps (E) Part II Review, p. 260


Index of Application xxiii

Kentucky Derby 2012 (E) Ch 1, p. 12 (E) Ch 1, p. 107 (E) Ch 6, p. 179

Marathon (E) Ch 22, p. 651–652, p. 655, p. 656 (E) Part VII Review, p. 890–891

Modalities (E) Ch 2, p. 35NBA draft lottery (E) Part III Review, p. 335NFL (E) Ch 5, p. 141NHL (E) Ch 3, p. 76, p. 77Par-4 hole tees (E) Part III Review, p. 335Playground (E) Ch 10, p. 298Push-ups (E) Ch 22, p. 652Racehorses (E) Ch 14, p. 405Running heats (E) Ch 21, p. 630Sidney Crosby (IE) Ch. 14, p. 387–389 (E)

Ch 14, p. 407Scottish hill races (E) Ch. 27, p. 837Shoes (E) Ch 11, p. 326SI jinx (E) Ch 7, p. 213Skating (E) Ch 25, p. 772Ski wax (E) Ch. 30, p. 894–895Skiing (FE) Ch. 5, p. 116–117Skydiving (E) Ch 11, p. 328Slalom times (E) Ch 3, p. 85–86Stanley Cup (E) Ch 1, p. 12–13 (E) Ch 7,

p. 275 (JC) Ch. 9, p. 267, p. 275Strikes (E) Ch 22, p. 654–655 (E) Ch 24,

p. 738Swimming (E) Ch 8, p. 244 (E) Ch 11,

p. 327 (E) Ch 21, p. 629–630 Swim the lake

Swimsuits (E) Ch 11, p. 326Toronto Blue Jay’s baseball players (FE)

Ch. 29, p. 913–914Toronto teams (E) Part I Review, p. 156Toronto Raptors (E) Ch 3, p. 79Tour de France (JC) Ch. 1, p. 7, p. 13 (E)

Ch 8, p. 246Winter Olympics 2010 (E) Ch 2, p. 34 (E)

(FE) Ch. 5, p. 119, p. 125, Ch 5, p. 140 (E) Part I Review, p. 152–153

Women’s basketball (E) Ch 4, p. 105Women’s Olympic alpine 2010 (E) Ch 3,

p. 80World Series (E) Ch 3, p. 77 (E) Part VI

Review, p. 699

Statistics68–95–99.7 rule (FE) Ch. 5, p. 125 (SBS)

Ch. 5, p. 126–127Adding the discounts (FE) Ch. 14, p. 383Analysis of variance (FE) Ch. 25,

p. 761 Ch. 26, p. 799 (SBS) Ch. 25, p. 759–761

Association (E) Ch 6, p. 178Assumptions and conditions (FE) Ch. 19,

p. 536 Ch. 20, p. 570–571 Ch. 21, p. 603–604

Binomial model (FE) Ch. 14, p. 390, p. 398–399 (SBS) Ch. 14, p. 390–391

Blinding (FE) Ch. 11, p. 312Blocking (FE) Ch. 11, p. 314Bootstrapping (SBS) Ch. 30, p. 938–940Boxplots (FE) Ch. 25, p. 747 Ch. 26, p. 791Causation and regression (FE) Ch. 8, p. 231Central Limit Theorem (FE) Ch. 15, p. 434

Ch. 20, p. 564Centre (FE) Ch. 3, p. 55, p. 65–66Chi-square mechanics (FE) Ch. 23, p. 674Chi-square test (FE) Ch. 23, p. 671–672,

p. 675 (E) Ch 23, p. 680 (SBS) Ch. 23, p. 662–663, p. 667–669, p. 671–673

Cluster sampling (FE) Ch. 10, p. 283Conditional distributions (FE) Ch. 2, p. 21Conditions for inference (FE) Ch. 17, p. 483Confidence interval (FE) Ch. 17, p. 489–490

Ch. 18, p. 513 Ch. 21, p. 604–605 (SBS) Ch. 17, p. 489

Confidence interval for proportion (SBS) Ch. 16, p. 459–460

Confounding (FE) Ch. 11, p. 317Contingency tables (SBS) Ch. 2, p. 24Correlation (FE) Ch. 6, p. 166 (JC) Ch. 6,

p. 166, p. 187 (E) Ch. 30, p. 946Correlation test (FE) Ch. 24, p. 721Decision errors (FE) Ch. 18, p. 515Discrete random variables (SBS) Ch. 14,

p. 380–381Disjoint and independent events (SBS)

Ch. 13, p. 362–363Distribution (FE) Ch. 4, p. 90 (JC) Ch. 3,

p. 52, p. 65, p. 86 (SBS) Ch. 3, p. 59Errors and power (FE) Ch. 18, p. 516Expected counts (FE) Ch. 23, p. 659Expected values (FE) Ch. 14, p. 377–378Expected values/mean (JC) Ch. 14, p. 379Experiments (SBS) Ch. 11, p. 307–308Experiment design (FE) Ch. 26, p. 782Extrapolation (FE) Ch. 8, p. 229F -test (FE) Ch. 25, p. 750General addition rule (SBS) Ch. 12,

p. 346–347Goodness-of-fit test (FE) Ch. 23, p. 661–662Groups comparison (SBS) Ch. 4, p. 90–91Histograms (FE) Ch. 3, p. 51 (E) Ch 3, p. 74Hypotheses (FE) Ch. 17, p. 482 (FE) Ch. 18,

p. 503 (SBS) Ch. 17, p. 485–487Indicator variables (FE) Ch. 28, p. 846–847Least squares (E) Ch 7, p. 210Linear model (FE) Ch. 7, p. 191Lurking variables (E) Part II Review,

p. 254–255Margin of error (FE) Ch. 16, p. 455, p. 456,

p. 457Marginal distributions (FE) Ch. 2, p. 20

Mean (FE) Ch. 14, p. 380, p. 382 (E) Ch. 30, p. 946

Median (FE) Ch. 3, p. 65–66 (E) Ch. 30, p. 946

Multiple regression (SBS) Ch. 27, p. 820–822, p. 824, p. 826–828 Ch. 28, p. 853–855, p. 858–865

Multiplication rule (SBS) Ch. 13, p. 358–360

Multistage sampling (FE) Ch. 10, p. 285–286

Nest Egg Index (FE) Ch. 4, p. 88–89Normal model (SBS) Ch. 5, p. 128–132Normal population assumption (FE) Ch. 22,

p. 636Observational study (FE) Ch. 11, p. 303,

p. 304–305One-factor experiment (FE) Ch. 25, p. 745One-proportion z-test (SBS) Ch. 18,

p. 504–506One-sample t -interval for mean (FE)

Ch. 20, p. 568 (SBS) Ch. 20, p. 572–573One-sample t-test for mean (FE) Ch. 20,

p. 575 (SBS) Ch. 20, p. 576–577Outliers (FE) Ch. 4, p. 93–94Paired data (FE) Ch. 22, p. 634Paired t -interval (FE) Ch. 22, p. 640–641Paired t -test (FE) Ch. 22, p. 639 (SBS)

Ch. 22, p. 637–639Paired-t confidence interval (FE) Ch. 22,

p. 643Pi (E) Ch 23, p. 681Plus four confidence interval (FE) Ch. 16,

p. 463–464Pooled t -test (FE) Ch. 21, p. 614–616Probability rules (JC) Ch. 12, p. 345P-value (FE) Ch. 17, p. 483, p. 484 Ch. 18,

p. 507, p. 508Range (E) Ch. 30, p. 946Regression (JC) Ch. 7, p. 200, p. 220 (E)

Ch 7, p. 220 (SBS) Ch. 7, p. 203–205Regression equation (FE) Ch. 7, p. 192,

p. 201 (SBS) Ch. 7, p. 193–194 (E) Ch 7, p. 210

Regression inference (FE) Ch. 24, p. 712 (SBS) Ch. 24, p. 707–708

Regression model (FE) Ch. 28, p. 851Regression slope t -test (SBS) Ch. 24,

p. 714–716Sample size & power (FE) Ch. 18, p. 521

(E) Ch 22, p. 656Sample size (FE) Ch. 16, p. 461, p. 462

Ch. 20, p. 580Sample survey (FE) Ch. 10, p. 278 (JC)

Ch. 10, p. 280, p. 286, p. 301 (SBS) Ch. 10, p. 287–288

Sampling distribution (FE) Ch. 15, p. 427


xxiv Index of Application

Sampling distribution model (FE) Ch. 15, p. 425 (JC) Ch. 15, p. 425 (SBS) Ch. 15, p. 426–427, p. 434–435

Sampling methods (FE) Ch. 10, p. 291Sampling strategy (FE) Ch. 10, p. 282–283,

p. 292SAT test (JC) Ch. 5, p. 119, p. 125, p. 145Scatterplot (FE) Ch. 6, p. 166 Ch. 8,

p. 233–234 (JC) Ch. 8, p. 226, p. 252 Ch. 7, p. 196

Scores standardization (JC) Ch. 5, p. 117Shape, centre, and spread (SBS) Ch. 3,

p. 63–64Shapes (E) Ch 3, p. 74Sign test (SBS) Ch. 20, p. 582–583Simple random samples (FE) Ch. 10, p. 281Simpson’s paradox (E) Ch 2, p. 42–43Simulation (E) Ch 5, p. 144 (SBS) Ch. 9,

p. 268–269Slope (FE) Ch. 24, p. 710–711Spatial distribution (E) Ch 2, p. 35–36Spread (FE) Ch. 3, p. 64Standard deviation (E) Ch 3, p. 76 (FE)

Ch. 14, p. 380, p. 382 (E) Ch. 30, p. 946

Standard error (FE) Ch. 19, p. 534–535 Ch. 21, p. 601–602

Standardized variables (SBS) Ch. 5, p. 120–121

Statistics Canada (JC) Ch. 20, p. 571–572, p. 578, p. 599

Timeplots and smoothing (FE) Ch. 4, p. 95–96

Trimmed mean (E) Ch 3, p. 79 (FE) Ch. 30, p. 935–936

Two-factor analysis of variance (FE) Ch. 26, p. 788–791

Two-proportion z -interval (FE) Ch. 19, p. 537–539

Two-proportion z-test (FE) Ch. 19, p. 541–542, p. 543–544

Two-sample t -interval (SBS) Ch. 21, p. 605–606

Two-sample t-test (FE) Ch. 21, p. 611 (SBS) Ch. 21, p. 608–610

Variables (FE) Ch. 2, p. 23Wilcoxon rank sum test (FE) Ch. 29, p. 901

(SBS) Ch. 29, p. 906–909

SurveysAmerican Association for Public Opinion

Research (AAPOR) (JC) Ch. 13, p. 360–361

Alcohol and drug surveys (E) Ch 3, p. 78–79

Automobiles (E) Ch 1, p. 11Birth order (E) Ch 12, p. 352Cell phone survey (E) Ch 10, p. 299Deaths (E) Ch 4, p. 104Emoticon use (E) Ch 10, p. 296Gallup Poll (E) Ch 10, p. 297–298Job satisfaction (E) Ch 21, p. 627, Ch 22,

p. 652Online behaviour of teens (FE) Ch. 19,

p. 534–535Mistaken polls (E) Ch 10, p. 298Parent opinion (E) Ch 10, p. 298Roper Consumer Poll (E) Ch 10, p. 296Sample survey (E) Ch 6, p. 184Stats students (E) Ch 1, p. 11Student centre survey (E) Ch 10, p. 296Television-viewing habits (SBS) Ch. 25,

p. 765–767 Ch. 26, p. 796–798Trudeaumania (E) Ch 1, p. 11TV watching (E) Ch 5, p. 140University survey (E) Part I Review, p. 148U.S. Census Bureau (FE) Ch. 8, p. 229Wording the survey (E) Ch 10, p. 299

TechnologyComputer-assisted ordering (E) Ch 23,

p. 685Computer use (IE) Part VI Review, p. 694Electronic Chip Production (E) Part II

Review, p. 258, p. 261Tablet Computers (E) Ch 24, p. 738

TelecommunicationArea codes (IE) Ch 1, p. 5Cell phones (IE) Ch 2, p. 39 (E)

Part III Review, p. 332Smart phones (IE) Ch 6, p. 183 Ch 9, p. 275

TransportAccidents (E) Part I Review, p. 150Air travel (E) Ch 1, p. 11Airfares (E) Part IV Review, p. 414Airport screening (E) Part VII Review,

p. 896Auto noise filters (E) Ch 25, p. 776Batteries (E) Ch. 26, p. 811–812Bikes (E) Ch 14, p. 411Brakes (E) Ch 7, p. 218

Braking (E) Ch 22, p. 653–654Car speeds (E) Ch 5, p. 139, p.140Cars (E) Ch 23, p. 685, Ch 24, p. 733,

p. 737Cockpit Noise (E) Ch 24, p. 734, p. 735Commuting (E) Ch 21, p. 625Crashes (E) Ch 11, p. 350 (E) Ch. 26, p. 806Driver’s licenses 2011 (E) Ch 2, p. 37Driver’s test (E) Ch 9, p. 274Driving on Friday the 13th (E) Ch 22,

p. 649, p. 650Drunk driving (E) Ch 4, p. 112 (E) Ch 11,

p. 375Fuel Economy (E) Ch 24, p. 733–735Gas additives (E) Ch. 26, p. 810, p. 811Gas mileage (E) Ch 11, p. 328 (E) Ch 8,

p. 239 (E) Ch 23, p. 681 (E) Ch. 26, p. 806

Gasoline (E) Ch 22, p. 653 (E) Ch. 29, p. 919

Highway safety study (FE) Ch. 13, p. 367Horsepower (E) Ch 3, p. 77 (E) Part II

Review, p. 257–258Motor vehicle crashes (FE) Ch. 18,

p. 510–511Motorcycles (FE) Ch. 8, p. 233–234New York bridges (E) Ch 7, p. 215–216Oakland passengers (E) Ch 8, p. 240Parking (E) Ch 20, p. 591 (E) Ch 21, p. 629Provincial Ministry of Transportation (FE)

Ch. 17, p. 482, p. 483, p. 484Red lights (E) Ch 14, p. 405Seatbelts (E) Ch 14, p. 407Speed (E) Ch 8, p. 242Teenage drivers (E) Ch 16, p. 473Teen traffic deaths (E) Ch. 28, p. 888–889Tires (E) Ch 5 p. 141–142 (E) Ch 9 p. 275Traffic delays (E) Ch. 28, p. 876Traffic headaches (E) Ch 6 p. 183 (E) Part II

Review, p. 256Trains (E) Ch 13, p. 372Vehicle weights (E) Ch 6, p. 181 Part II

Review, p. 261

WebAmazon.com (IE) Ch 1, p. 3E-mails (IE) Ch 3, p. 75Facebook (IE) Ch 11 p. 323 (E) Ch 13

p. 370 (E) Part IV Review p. 414Internet source (E) Ch 1, p. 10Junk mail (IE) Ch 16, p. 473Mail (IE) Part I Review, p. 154


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