Ebook Statistics (12th edition): Part 1

(BQ) Part 1 book Statistics has contents: Statistics, data, and statistical thinking; methods for describing sets of data; probability; discrete random variables; continuous random variables, sampling distributions; inferences based on a single sample - Estimation with confidence intervals; inferences based on a single sample - Tests of hypothesis.

Standard deviation Investigates how distribution shape and

spread affect standard deviation

Students visualize relationship between mean and standard deviation by adding and deleting data points; applet updates mean and standard deviation

Simulates selecting 100 random samples from the population and finds the 95% and 99%

confidence intervals for each Students specify population proportion and sample size; applet plots confidence intervals and reports number and proportion containing true proportion

Simulates selecting 100 random samples from the population and finds the 95% z -interval and 95% t -interval for each Students specify sample size, distribution shape, and population mean and standard deviation; applet plots confidence intervals and reports number and proportion containing true mean

Simulates selecting 100 random samples from population; calculates and plots z- statistic and

P -value for each Students specify population proportion, sample size, and null and alternative hypotheses; applet reports number and proportion of times null hypothesis is rejected at 0.05 and 0.01 levels

Simulates selecting 100 random samples from population; calculates and plots t statistic and

P -value for each Students specify population distribution shape, mean, and standard deviation; sample size, and null and alternative hypotheses; applet reports number and proportion of times null hypothesis is rejected

at both 0.05 and 0.01 levels

8.1 , 360; 8.2 , 364; 8.3 , 364; 8.4 ,

364

Correlation by eye Correlation coefficient measures strength of

linear relationship between two variables

Teaches user how to assess strength of a linear relationship from a scattergram

Computes correlation coefficient r for a set

of bivariate data plotted on a scattergram

Students add or delete points and guess value

of r ; applet compares guess to calculated value

11.2 , 585

Regression by eye The least squares regression line has a

smaller SSE than any other line that might approximate a set of bivariate data Teaches students how to approximate the location of

a regression line on a scattergram

Computes least squares regression line for a set of bivariate data plotted on a scattergram

Students add or delete points and guess location of regression line by manipulating a line provided on the scattergram; applet plots least squares line and displays the equations and the SSEs for both lines

11.1 , 561

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ISBN 10: 0-321-75593-6 ISBN 13: 978-0-321-75593-3 www.pearsonhighered.com

Contents

Preface xii Applications Index xvii

1.1 The Science of Statistics 2

1.2 Types of Statistical Applications 3

1.3 Fundamental Elements of Statistics 5

1.4 Types of Data 9

1.5 Collecting Data 11

1.6 The Role of Statistics in Critical Thinking and Ethics 14

Statistics in Action: Social Media Networks and the Millennial Generation 2

Using Technology: Accessing and Listing Data 22

2.1 Describing Qualitative Data 27

2.2 Graphical Methods for Describing Quantitative Data 37

2.3 Summation Notation 49

2.4 Numerical Measures of Central Tendency 50

2.5 Numerical Measures of Variability 61

2.6 Interpreting the Standard Deviation 66

2.7 Numerical Measures of Relative Standing 73

2.8 Methods for Detecting Outliers: Box Plots and z -Scores 78

2.9 Graphing Bivariate Relationships (Optional) 87

2.10 Distorting the Truth with Descriptive Statistics 92

Statistics in Action: Body Image Dissatisfaction: Real or Imagined? 26

Using Technology: Describing Data 105

3.1 Events, Sample Spaces, and Probability 110

3.2 Unions and Intersections 123

viii

3.8 Some Additional Counting Rules (Optional) 154

3.9 Bayes’s Rule (Optional) 164

Statistics in Action: Lotto Buster! Can You Improve Your Chance of Winning? 109

Using Technology: Generating a Random Sample; Combinations and Permutations 177

7.1 Identifying and Estimating the Target Parameter 299

7.2 Confidence Interval for a Population Mean: Normal ( z ) Statistic 301

7.3 Confidence Interval for a Population Mean: Student’s t -Statistic 310

7.4 Large-Sample Confidence Interval for a Population Proportion 320

Chapter 7

Inferences Based on a Single Sample:

4.1 Two Types of Random Variables 181

4.2 Probability Distributions for Discrete Random Variables 184

4.3 Expected Values of Discrete Random Variables 190

4.4 The Binomial Random Variable 195

4.5 The Poisson Random Variable (Optional) 207

4.6 The Hypergeometric Random Variable (Optional) 212

Statistics in Action: Probability in a Reverse Cocaine Sting: Was Cocaine Really Sold? 180

Using Technology: Discrete Random Variables and Probabilities 222

5.1 Continuous Probability Distributions 225

5.2 The Uniform Distribution 227

5.3 The Normal Distribution 231

5.4 Descriptive Methods for Assessing Normality 244

5.5 Approximating a Binomial Distribution with a Normal Distribution

(Optional) 252

5.6 The Exponential Distribution (Optional) 257

Statistics in Action: Super Weapons Development—Is the Hit Ratio Optimized? 225

Using Technology: Continuous Random Variables, Probabilities, and Normal Probability Plots 268

6.1 The Concept of a Sampling Distribution 273

6.2 Properties of Sampling Distributions: Unbiasedness and Minimum

Variance 279

6.3 The Sampling Distribution of x¯ and the Central Limit Theorem 283

Statistics in Action: The Insomnia Pill: Is It Effective? 272

Using Technology: Simulating a Sampling Distribution 296

Chapter 8

Inferences Based on a Single Sample:

8.1 The Elements of a Test of Hypothesis 350

8.2 Formulating Hypotheses and Setting Up the Rejection Region 356

8.3 Test of Hypothesis about a Population Mean: Normal

(z ) Statistic 361

8.4 Observed Significance Levels: p -Values 367

8.5 Test of Hypothesis about a Population Mean: Student’s t -Statistic 373

8.6 Large-Sample Test of Hypothesis about a Population Proportion 380

8.7 Calculating Type II Error Probabilities: More about b (Optional) 387

8.8 Test of Hypothesis about a Population Variance (Optional) 396

Statistics in Action: Diary of a KLEENEX ® User—How Many Tissues in a Box? 350

Using Technology: Tests of Hypotheses 406

Chapter 9

Inferences Based on a Two Samples:

9.1 Identifying the Target Parameter 410

9.2 Comparing Two Population Means: Independent Sampling 411

9.3 Comparing Two Population Means: Paired Difference Experiments 428

9.4 Comparing Two Population Proportions: Independent Sampling 440

9.5 Determining the Sample Size 447

9.6 Comparing Two Population Variances: Independent Sampling

(Optional) 450

Statistics in Action: ZixIt Corp v Visa USA Inc.—A Libel Case 410

Using Technology: Two-Sample Inferences 468

7.5 Determining the Sample Size 327

7.6 Confidence Interval for a Population Variance (Optional) 334

Statistics in Action: Medicare Fraud Investigations 299

Using Technology: Confidence Intervals 346

Chapter 10

Analysis of Variance:

Comparing More than Two Means 474 10.1 Elements of a Designed Study 476

10.2 The Completely Randomized Design: Single Factor 481

10.3 Multiple Comparisons of Means 497

10.4 The Randomized Block Design 505

10.5 Factorial Experiments: Two Factors 519

Statistics in Action: On the Trail of the Cockroach: Do Roaches Travel at Random? 475

Using Technology: Analysis of Variance 547

11.5 The Coefficients of Correlation and Determination 578

11.6 Using the Model for Estimation and Prediction 588

11.7 A Complete Example 596

Statistics in Action: Can "Dowsers" Really Detect Water? 550

Using Technology: Simple Linear Regression 610

12.1 Multiple-Regression Models 614

PART I: First-Order Models with Quantitative Independent Variables

12.2 Estimating and Making Inferences about the ß Parameters 616

12.3 Evaluating Overall Model Utility 623

12.4 Using the Model for Estimation and Prediction 633

PART II: Model Building in Multiple Regression

12.5 Interaction Models 639

12.6 Quadratic and Other Higher Order Models 646

12.7 Qualitative (Dummy) Variable Models 656

12.8 Models with Both Quantitative and Qualitative Variables (Optional) 663

12.9 Comparing Nested Models (Optional) 672

12.10 Stepwise Regression (Optional) 681

PART III: Multiple Regression Diagnostics

12.11 Residual Analysis: Checking the Regression Assumptions 687

12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation 701

Statistics in Action: Modeling Condominium Sales: What Factors Affect Auction Price? 613

Using Technology: Multiple Regression 722

13.1 Categorical Data and the Multinomial Experiment 726

13.2 Testing Categorical Probabilities: One-Way Table 728

13.3 Testing Categorical Probabilities: Two-Way (Contingency) Table 736

13.4 A Word of Caution about Chi-Square Tests 751

Statistics in Action: College Students and Alcohol: Is Amount Consumed Related to Drinking

Frequency? 726

Using Technology: Chi-Square Analyses 759

Chapter 14 Nonparametric Statistics (available on CD) 14-1

14.1 Introduction: Distribution-Free Tests 14-2

14.2 Single-Population Inferences 14-4

14.3 Comparing Two Populations: Independent Samples 14-10

14.4 Comparing Two Populations: Paired Difference Experiment 14-18

14.5 Comparing Three or More Populations: Completely Randomized Design 14-27

14.6 Comparing Three or More Populations: Randomized Block Design 14-34

Table VI Critical Values of t 775 Table VII Critical Values of x2 776 Table VIII Percentage Points of the F-Distribution, a = 10 778 Table IX Percentage Points of the F-Distribution, a = 05 780 Table X Percentage Points of the F-Distribution, a = 025 782 Table XI Percentage Points of the F-Distribution, a = 01 784 Table XII Critical Values of TL and TU for the Wilcoxon Rank Sum Test:

Independent Samples 786 Table XIII Critical Values of T0 in the Wilcoxon Paired Difference Signed

Rank Test 787 Table XIV Critical Values of Spearman’s Rank Correlation Coefficient 788 Table XV Critical Values of the Studentized Range, a = 0.5 789

Table XVI Critical Values of the Studentized Range, a = 0.1 790

Appendix B Calculation Formulas for Analysis of Variance 791

Preface

Statistics is an introductory text that emphasizes inference and sound decision-making

through extensive coverage of data collection and analysis As in earlier editions, the twelfth edition text stresses the development of statistical thinking, the assessment of credibility, and value of the inferences made from data, both by those who consume and those who produce them It assumes a mathematical background of basic algebra The text incorporates the following strategies, developed from the American Statistical Association’s (ASA) Guidelines for Assessment and Instruction in Statistics Education (GAISE) Project:

• Emphasize statistical literacy and develop statistical thinking

• Use real data in applications

• Use technology for developing conceptual understanding and analyzing data

• Foster active learning in the classroom

• Stress conceptual understanding rather than mere knowledge of procedures

A briefer version of the book, A First Course in Statistics , is available for single-

semester courses that include minimal coverage of regression analysis, analysis of ance, and categorical data analysis

vari-Content-Specific Changes to This Edition

• Chapter 7 (Confidence Intervals). The methodology for finding a confidence

interval for a population mean is developed based on using either a normal (z) statistic ( Section 7.2 ) or a Student’s t-statistic ( Section 7.3 ) Also, we added an

optional section ( Section 7.6 ) on estimating a population variance

• Chapter 8 (Tests of Hypothesis). A new section emphasizing the formulation of the null and alternative hypotheses ( Section 8.2 ) has been added

New in the Twelfth Edition

• More than 1,800 exercises, with revisions and updates to 20%. Many new and updated exercises, based on contemporary studies and real data, have been added Most of these exercises foster and promote critical thinking skills

• Updated technology. All printouts from statistical software (SAS®, IBM® SPSS®, MINITAB®, and the TI-83/84 Plus Graphing Calculator) and corresponding instruc- tions for use have been revised to reflect the latest versions of the software

• New and Revised Statistics in Action Cases. More than one-third of the Statistics

in Action cases are new or revised, each based on real data from a recent study

• Redesigned end-of-chapter summaries. Summaries at the end of each ter have been redesigned to make them better study aids for students Important points are reinforced through flow graphs (which aid in selecting the appropriate statistical method) and boxed notes with key ideas, terms, symbols/notation, and formulas

• Emphasis on ethics. Where appropriate, boxes have been added emphasizing the importance of ethical behavior when collecting, analyzing, and interpreting data

• Learning objectives. Chapter opening Where We’re Going bullet points include

section numbers that correspond to where that concept is discussed in the chapter

• Chapter 12 (Multiple Regression and Model Building). For pedagogical poses, the chapter is divided into three parts: First Order Models with Quantitative Independent Variables, Model Building, and Multiple Regression Diagnostics

• Chapter 13 (Categorical Data Analysis). A subsection on contingency tables with fixed marginals as been added to Section 13.3

Hallmark Strengths

We have maintained or strengthened the pedagogical features that make Statistics

unique among introductory statistics texts These features, which assist the student in achieving an overview of statistics and an understanding of its relevance in the world and everyday life, are as follows:

• Use of examples as a teaching device. Almost all new ideas are introduced and illustrated by data-based applications and examples We believe that students bet-

ter understand definitions, generalizations, and theoretical concepts after seeing an

application All examples have three components: (1) Problem, (2) Solution, and (3) Look Back (or Look Ahead) This step-by-step process provides students with a defined structure by which to approach problems and enhances their problem-solv- ing skills The Look Back feature often gives helpful hints to solving the problem and/or provides a further reflection or insight into the concept or procedure that is covered.

• Now Work. A Now Work exercise suggestion follows each example The Now

Work exercise (marked with the icon NW in exercise sets) is similar in style and cept to the text example This provides students with an opportunity to immediately test and confirm their understanding

• Statistics in Action Each chapter begins with a case study based on an actual temporary, controversial or high-profile issue Relevant research questions and data

con-from the study are presented and the proper analysis demonstrated in short Statistics

in Action Revisited sections throughout the chapter These motivate students to

critically evaluate the findings and think through the statistical issues involved

• Applet Exercises. The text is accompanied by a resource CD containing applets (short JAVA computer programs) These point-and-click applets allow students to easily run simulations that visually demonstrate some of the more difficult statisti- cal concepts (e.g., sampling distributions and confidence intervals.) Each chapter contains several optional applet exercises in the exercise sets They are denoted with the following icon:

• Real data-driven exercises. The text includes more than 1,800 exercises based on

a wide variety of applications in various disciplines and research areas Nearly all

of the applied exercises use current, real data extracted from newspapers, magazines, current journals, and the Internet Some students have difficulty learning the mechanics of statistical techniques when all problems are couched in terms of realistic applications For this reason, all exercise sections are divided into four parts:

Learning the Mechanics Designed as straightforward applications of new concepts, these exercises allow students to test their ability to comprehend a mathematical concept or a definition

Applying the Concepts—Basic Based on applications taken from a wide

vari-ety of journals, newspapers, and other sources, these short exercises help students begin developing the skills necessary to diagnose and analyze real-world problems

Applying the Concepts—Intermediate Based on more detailed real-world

applications, these exercises require students to apply their knowledge of the technique presented in the section

Applying the Concepts—Advanced These more difficult real-data exercises

require students to utilize their critical thinking skills

xiv

• Critical Thinking Challenges. Placed at the end of the Supplementary Exercises section only, students are asked to apply their critical thinking skills to solve one or two challenging real-life problems These exercises expose students to real-world problems with solutions that are derived from careful, logical thought and selection

of the appropriate statistical analysis tool

• Exploring data with statistical computer software and the graphing calculator.

We demonstrate each statistical analysis method presented using output from three leading statistical software packages: SAS, SPSS, and MINITAB These outputs appear throughout the text in examples and exercises, exposing students to the out- put they will encounter in today’s high-tech world In addition, we provide output

and keystroke instructions for the TI-83/84 Plus Graphing Calculator in the Using

Technology section at the end of appropriate chapters

• Using Technology tutorials. At the end of each chapter we’ve included cal software tutorials with instructions and screen shots for MINITAB and, where appropriate, the TI-83/84 Plus Graphing Calculator These step-by-step tutorials are easily located and show students how to best use statistical software

• Biographies. Brief descriptions of famous statisticians and his/her achievements are presented in-text and in marginal boxes With these profiles, students will develop an appreciation for the statistician’s efforts and the discipline of statistics as a whole

• CD-ROM. New copies of the text are accompanied by a resource CD that contains files for all of the text examples, exercises, Statistics in Action, and Real-World case data sets marked with a Data sets are provided in multiple formats The CD also contains Chapter 14 , Nonparametric Statistics, and a set of applets that illustrate statistical concepts

intro-it a difficult subject to comprehend We believe that one cause for these problems is the mixture of probability and counting rules that occurs in most introductory texts Consequently, we have included the counting rules (with examples) in an appendix ( Appendix A ) rather than in the body of Chapter 3 Thus, the instructor can control the level of coverage of probability covered

• Multiple regression and model building. This topic represents one of the most useful statistical tools for the solution of applied problems Although an entire text could be devoted to regression modeling, we feel that we have presented coverage that is understandable, usable, and much more comprehensive that the presenta- tions in other introductory statistics texts We devote two full chapters to discuss- ing the major types of inferences that can be derived from a regression analysis, showing how these results appear in the output from statistical software, and, most important, selecting multiple regression models to be used in an analysis Thus, the instructor has the choice of a one-chapter coverage of simple linear regression ( Chapter 11 ), a two-chapter treatment of simple and multiple regression (excluding the sections on model building in Chapter 12 ), or complete coverage of regression analysis, including model building and regression diagnostics This extensive cover- age of such useful statistical tools will provide added evidence to the student of the relevance of statistics to real-world problems

• Role of calculus in footnotes. Although the text is designed for students without

a calculus background, footnotes explain the role of calculus in various derivations Footnotes are also used to inform the student about some of the theory underlying certain methods of analysis These footnotes allow additional flexibility in the math- ematical and theoretical level at which the material is presented

Supplements

Student Resources

Student’s Solutions Manual, by Nancy Boudreau (Bowling

Green State University), includes complete worked out

solutions to all odd-numbered text exercises (ISBN-13:

978-0-321-75597-1; ISBN-10: 0-321-75597-9)

Excel®Manual (download only), by Mark Dummeldinger

(University of South Florida) Available for download

from www.pearsonhighered.com/mathstatsresources

MINITAB®Manual (download only), by Keith Bower,

available for download from www.pearsonhighered.com/

mathstatsresources

Graphing Calculator Manual (download only), by Susan

Herring (Sonoma State University), available for

down-load from www.pearsonhighered.com/mathstatsresources

Study Cards for Statistics Software This series of study

cards, available for Excel, MINITAB, JMP®, SPSS, R,

StatCrunch®, and TI-83/84 Plus Graphing Calculators

pro-vides students with easy step-by-step guides to the most

common statistics software Visit myPearsonstore.com for

more information

Instructor Resources

Annotated Instructor’s Edition contains answers to text

exercises Annotated marginal notes include Teaching

Tips, suggested exercises to reinforce the statistical

con-cepts discussed in the text, and short answers to exercises

and examples (ISBN-13: 978-0-321-75694-7; ISBN-10:

0-321-75694-0)

Instructor’s Solutions Manual, by Nancy Boudreau

(Bowling Green State University), includes complete

worked-out solutions to all even-numbered text exercises

Careful attention has been paid to ensure that all

meth-ods of solution and notation are consistent with those used

in the core text (ISBN-13: 978-0-321-78340-0; ISBN-10:

0-321-78340-9).

PowerPoint® Lecture Slides include figures and tables

from the textbook Available for download from Pearson’s

online catalog at www.pearsonhighered.com/irc

TestGen® ( www.pearsoned.com/testgen ) enables

instruc-tors to build, edit, print, and administer tests using a

com-puterized bank of questions developed to cover all the

objectives of the text TestGen is algorithmically based,

allowing instructors to create multiple but equivalent

ver-sions of the same question or test with the click of a

but-ton Instructors can also modify test bank questions or add

new questions The software and test bank are available for download from Pearson Education’s online catalog

Online Test Bank, a test bank derived from TestGen ®, is available for download from Pearson’s online catalog at

www.pearsonhighered.com/irc

The Pearson Math Adjunct Support Center ( http://www pearsontutorservices.com/math-adjunct.html ) is staffed by qualified instructors with more than 100 years of combined experience at both the community college and university lev- els Assistance is provided for faculty in the following areas:

• Suggested syllabus consultation

• Tips on using materials packed with your book

• Book-specific content assistance

• Teaching suggestions, including advice on classroom strategies

Technology Resources

A companion CD-ROM is bound in new copies of Statistics

The CD holds a number of support materials, including:

• Data sets formatted as csv, txt, and TI files

• Applets (short JAVA computer programs) that allow

students to run simulations that visually demonstrate statistical concepts

• Chapter 14 : Nonparametric Statistics

Data sets are also available for download from www pearsonhighered.com/mathstatsresources

MathXL® for Statistics Online Course (access code quired) MathXL ® is the homework and assessment engine that runs MyStatLab (MyStatLab is MathXL plus a learn- ing management system.) With MathXL for Statistics, in- structors 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 alized study plans and/or personalized homework assignments based on their test results

xvi

using the powerful statistical software, and generate compelling reports

• Integration of Statistical Software Knowing that

stu-dents often use external statistical software, we make

it easy to copy our data sets, both from the ebook and MyStatLab questions, into software like StatCrunch, MINITAB, Excel and more Students have access to

a variety of support—Technology Instruction Videos, Technology Study Cards, and Manuals—to learn how to effectively use statistical software

• Expert Tutoring Although many students describe

the whole of MyStatLab as “like having your own sonal tutor,” students also have access to live tutoring from Pearson Qualified statistics instructors provide tutoring sessions for students via MyStatLab

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 knowing that you are using quality content That means that our eTexts are accurate, that our assess- ment tools work, and that our questions are error- free And whether you are just getting started with MyStatLab, or have a question 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

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 13,000 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 statistical concepts, and are available for export to en- rich 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 a MyStatLab kit, and StatCrunch is available by itself to qualified adopters For more information, visit our website at www statcrunch.com , or contact your Pearson representative

The Student Edition of MINITAB is a condensed edition

of the professional release of MINITAB statistical software

• 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

software, we make it easy to copy our data sets, both

from the ebook 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

MyStatLab ™ 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,

tradi-tional—and demonstrates the quantifiable difference

that integrated usage has on student retention,

subse-quent success, and overall achievement

• MyStatLab’s comprehensive online gradebook

automatically tracks students’ results on tests, quizzes,

homework, and in the study plan Instructors can use

the gradebook to intervene if students have trouble or

to provide positive feedback Data can be easily

ex-ported to a variety of spreadsheet programs, such as

Microsoft Excel

MyStatLab provides engaging experiences that personalize,

stimulate, and measure learning for each student

• Tutorial Exercises with Multimedia Learning Aids.

The homework and practice exercises in MyStatLab

align with the exercises in the textbook, and they

re-generate 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

ex-tra help at point-of-use

• Getting Ready for Statistics A library of questions now

appears within the MyStatLab assessment manager to

offer the developmental math topics students need for

the course These can be assigned as a prerequisite to

other assignments, if desired

• Conceptual Question Library In addition to

algorith-mically regenerated questions that are aligned with

your textbook, there is a library of 1,000 Conceptual

Questions available in the assessment managers that

re-quire students to apply their statistical understanding

• StatCrunch MyStatLab includes a web-based

statisti-cal software, StatCrunch, within the online assessment

platform so that students can easily analyze data sets

from exercises and the text In addition, MyStatLab

includes access to www.statcrunch.com , a web site

where users can access more than 13,000 shared data

sets, conduct online surveys, perform complex analyses

This book reflects the efforts of a great many people over a number of years First, we would like to thank the following professors, whose reviews and comments on this and prior editions have contributed to the 12th edition:

Reviewers Involved with the Twelfth Edition of Statistics

Ali Arab, Georgetown University Jen Case, Jacksonville State University Maggie McBride, Montana State University—Billings Surajit Ray, Boston University

JR Schott, University of Central Florida Susan Schott, University of Central Florida Lewis Shoemaker, Millersville University Engin Sungur, University of Minnesota—Morris Sherwin Toribio, Universitiy of Wisconsin—La Crosse Michael Zwilling, Mt Union College

Reviewers of Previous Editions

Bill Adamson, South Dakota State; Ibrahim Ahmad, Northern Illinois sity; Roddy Akbari, Guilford Technical Community College; David Atkinson, Olivet Nazarene University; Mary Sue Beersman, Northeast Missouri State University; William H Beyer, University of Akron; Marvin Bishop, Manhattan College; Patricia M Buchanan, Pennsylvania State University; Dean S Burbank, Gulf Coast Community College; Ann Cascarelle, St Petersburg College; Kathryn Chaloner, University of Minnesota; Hanfeng Chen, Bowling Green State Univer- sity; Gerardo Chin-Leo, The Everygreen State College; Linda Brant Collins, Iowa State University; Brant Deppa, Winona State University; John Dirkse, California State University—Bakersfield; N B Ebrahimi, Northern Illinois University; John Egenolf, University of Alaska—Anchorage; Dale Everson, University of Idaho; Christine Franklin, University of Georgia; Khadiga Gamgoum, Northern Virginia CC; Rudy Gideon, University of Montana;Victoria Marie Gribshaw, Seton Hill College; Larry Griffey, Florida Community College; David Groggel, Miami Uni- versity at Oxford; Sneh Gulati, Florida International University; John E Groves, California Polytechnic State University—San Luis Obispo; Dale K Hathaway, Olivet Nazarene University; Shu-ping Hodgson, Central Michigan University; Jean L Holton,Virginia Commonwealth University; Soon Hong, Grand Valley; Ina Parks S Howell, Florida International University; Gary Itzkowitz, Rowan College of New Jersey; John H Kellermeier, State University College at Platts- burgh; Golan Kibria, Florida International University;Timothy J Killeen, Uni- versity of Connecticut; William G Koellner, Montclair State University; James R Lackritz, San Diego State University; Diane Lambert,AT&T/Bell Laboratories; Edwin G Landauer, Clackamas Community College; James Lang,Valencia Junior College; Glenn Larson, University of Regina; John J Lefante, Jr., University of South Alabama; Pi-Erh Lin, Florida State University; R Bruce Lind, Univer- sity of Puget Sound; Rhonda Magel, North Dakota State University; Linda C

Univer-Acknowledgments

It offers the full range of statistical methods and

graphi-cal capabilities, along with worksheets that can include

up to 10,000 data points Individual copies of the software

can be bundled with the text (ISBN-13: 978-0-321-11313-9;

ISBN-10: 0-321-11313-6 )

JMP Student Edition is an easy-to-use, streamlined

ver-sion of JMP desktop statistical discovery software from SAS Institute Inc and is available for bundling with the text (ISBN-13: 978-0-321-67212-4 ISBN-10: 0-321-67212-7)

xviii

Malone, University of Central Florida; Allen E Martin, California State sity—Los Angeles; Rick Martinez,Foothill College; Brenda Masters, Oklahoma State University; Leslie Matekaitis, Cal Genetics; E Donice McCune, Stephen

Univer-F Austin State University; Mark M Meerschaert, University of Nevada—Reno; Greg Miller, Steven F Austin State University; Satya Narayan Mishra, University

of South Alabama; Kazemi Mohammed, UNC–Charlotte; Christopher Morrell, Loyola College in Maryland; Mir Mortazavi, Eastern New Mexico University;A Mukherjea, University of South Florida; Steve Nimmo, Morningside College (Iowa); Susan Nolan, Seton Hall University;Thomas O’Gorman, Northern Il- linois University; Bernard Ostle, University of Central Florida; William B Owen, Central Washington University;Won J Park, Wright State University; John J Peterson, Smith Kline & French Laboratories; Ronald Pierce, Eastern Kentucky University; Betty Rehfuss, North Dakota State University—Bottineau; Andrew Rosalsky, University of Florida; C Bradley Russell, Clemson University; Rita Schillaber,University of Alberta; James R Schott, University of Central Florida; Susan C Schott, University of Central Florida; George Schultz, St Petersburg Junior College; Carl James Schwarz, University of Manitoba; Mike Seyfried, Shippensburg University; Arvind K Shah, University of South Alabama; Lewis Shoemaker, Millersville University; Sean Simpson, Westchester CC; Charles W Sinclair, Portland State University; Robert K Smidt, California Polytechnic State University—San Luis Obispo; Vasanth B Solomon, Drake University; W Robert Stephenson, Iowa State University; Thaddeus Tarpey, Wright State University; Kathy Taylor, Clackamas Community College; Barbara Treadwell, Western Michi- gan University; Dan Voss, Wright State University; Augustin Vukov, University of Toronto; Dennis D Wackerly, University of Florida; Barbara Wainwright, Salis- bury University; Matthew Wood, University of Missouri—Columbia

Other Contributors

Special thanks are due to our ancillary authors, Nancy Boudreau, Mark Dummeldinger, Keith Bower, and Susan Herring Thank you to our accuracy checkers, Engin Sungur and Cathleen Zucco-Teveloff, who helped to insure a highly accurate, clean text Finally, the Pearson staff of Deirdre Lynch, Marianne Stepanian, Tracy Patruno, Dana Bettez, Sonia Ashraf, Barbara Atkinson, Jean Choe, Kathleen DeChavez, Roxanne McCarley, and Erin Lane, and Integra-Chicago’s Amanda Zagnoli all helped greatly with various stages of the book and media

Beverage applications:

alcohol drinking frequency, 714 alcohol marriage study, 540 – 541 Bordeaux wine sold at auction,

632 – 633 bottled water analysis, 206 , 256 caffeine content of coffee, 333 Pepsi vs Coca-Cola, 7 – 9 restoring self-control when intoxi-cated, 495 – 496 , 505

spoiled wine testing, 216 undergraduate problem drinking, 309

Biology/life science applications:

African rhinos, 121 antigens for parasitic roundworm in birds, 319 , 338

bacteria in bottled water, 333 bacteria-infected spider mites, reproduction of, 319 bear vs pig bile study, 462 beetles and slime molds, 734 bird nest holes, competition for, 98 – 99

birds feeding on gypsy moths, 756 birds in butterfly hot spots, 220 bird species abundance, 720 body length of armadillos, 45 butterfly hot spots, 755 cheek teeth of extinct primates, 34 , 46 ,

59 , 65 – 66 , 121 – 122 , 161 – 162 , 338 ,

378 – 379 chemical insect attractant, 172 chemical signals of mice, 134 ,

205 – 206 , 257 cockatiels’ taste preferences, 481 cockroach random-walk theory, 475 ,

491 – 492 , 501 – 502 , 530 – 531 contaminated fish, 167 , 264 , 335 – 337 ,

541 , 630 – 631 , 638 , 708 – 709 corn in duck diet, 686 – 687 crab spiders hiding on flowers,

47 – 48 , 378 DNA-reading tool for quick identification of species, 360 – 361 ecotoxicological survival, 256 – 257 egg shell quality in laying hens, 533 extinct birds, 19 , 37 , 72 , 77 , 147 , 216 ,

308 , 480 , 662 fallow deer bucks’ probability of fighting, 133 , 147

fish feeding behavior, 89 – 90 , 603 – 604 geese decoy effectiveness, 544 glacial drifts, 86

great white shark lengths, 380 grizzly bear habitats, 718 habitats of endangered species, 251 ice melt ponds, 35 – 36 , 326 , 720 , 735 identifying organisms using computers, 386

inbreeding of tropical wasps, 343 , 404 Index of Biotic Integrity, 424

Agricultural/gardening/farming

applications:

chickens with fecal contamination, 207

colored string preferred by chickens,

308 – 309 , 372 – 373

crop yield comparisons, 447 – 448

egg shell quality in laying hens, 533

eggs produced from different housing

systems, 543

endangered dwarf shrubs, 516 – 517

fungi in beech forest trees, 171 , 220 ,

pig farmer study, 755

plants and stress reduction, 518

rat damage to sugarcane, 450

RNA analysis of wheat genes, 670 – 671

subarctic plant study, 749

trapping grain moths, 748

USDA chicken inspection, 121

zinc phosphide in pest control, 103

Archaeological applications:

ancient pottery, 36 , 147 , 341 , 734

bone fossil study, 366 , 372

footprints in sand, 685 – 686

ring diagrams study, 100

selecting dig sites, 154

Astronomy/space science applications:

alien spacecraft poll, 21

astronomy students and the Big Bang

theory, 386

deep-space survey of quasars,

631 – 632 , 637

galaxies velocity, 264 , 266

lunar soil study, 386

rare planet transits, 211

redshifts of quasi-stellar objects,

563 , 586

satellites in orbit, 36

space shuttle disaster, 221

speed of light from galaxies, 101 ,

Automotive/motor vehicle applications:

air bag danger to children, 344 – 345

air-pollution standards for engines,

375 – 376

ammonia in car exhaust, 100 , 342

automobiles stocked by dealers, 163

car battery guarantee, 69 – 70

car wash waiting time, 212

cell phone use by drivers, 342 , 402

critical-part failures in NASCAR vehicles, 260 , 291

drivers stopped by police, 76 driving routes, 156

emergency rescue vehicle use, 219 fatal car crashes and speeding, 146 gas mileage advertising, 237 – 238 ,

245 – 247 , 395 highway crash data, 632 impact of red light cameras on car crashes, 438

income and road rage, 542 motorcyclists and helmets, 15 new-car crash testing, 98 , 171 , 183 , 188 ,

194 , 243 , 251 , 437 satellite radio in cars, 20 , 325 , 385 , 396 speeding and young drivers, 364 – 365 ,

371 – 372 testing tires for wear, 653 unleaded fuel costs, 291 used-car warranties, 228 – 229

Aviation applications:

airline fatalities, 211 airline shipping routes, 155 battle simulation trials, 757 classifying air threats with heuristics,

748 – 749 “cry wolf” effect in air traffic control-ling, 747

flight response of geese to helicopter traffic, 755 – 756

luggage inspection at Newark airport, 257

Behavioral study applications See also

Psychological applications

accountants and Machiavellian traits,

365 , 539 – 540 adolescents with ADHD, 629 – 630 alcohol threats and electric shocks, 244 bullying behavior study, 445

dating and disclosure, 21 , 365 ,

628 – 629 , 706 divorced couples study, 116 – 117 income and road rage, 542 infants’ listening time, 360 jail suicide risk, 103 , 104 , 344 motivation of drug dealers, 71 – 72 , 76 ,

183 , 291 , 307 , 337 – 338 , 400 parents’ behavior at a gym meet, 220 personality and aggressive behavior,

308 , 707 quit-smoking program, 219 rat-in-maze experiment, 67 – 68 rudeness in the workplace, 427 shock treatment to learners, 139 teacher perceptions of child behavior,

365 , 372 violent behavior in children, 653 violent song lyrics and aggression, 154 walking in circles when lost, 380

Applications Index

sale prices of apartments, 718 – 719 sale prices of homes, predicting,

601 – 602 spall damage in bricks, 609

Crime applications:

casino employment and, 580 – 581 community responses to violent,

662 – 663 computer, 20 masculinity and, 427 , 747 Medicare fraud investigations, 299 ,

315 – 316 , 324 , 331 proportion who are victims of violent,

323 – 324

Dental applications:

acidity of mouthwash, 437 anesthetics, dentists’ use of, 72 , 86 , 291 dental visit anxiety, 242 , 378 , 504 – 505 laughing gas usage, 219

new dental bonding agent, 379 , 399 ,

494 , 503 – 504 teeth defects and stress in prehistoric Japan, 447

Earth science applications:

albedo of ice melt ponds, 308 alkalinity of river water, 264 , 403 daylight duration in western Pennsylvania, 317 – 318 , 333 dissolved organic compound in lakes, 366

earthquake aftershocks, 55 – 56 earthquake ground motion, 18 – 19 glacial drifts, 86 , 496 – 497 , 505 glacier elevations, 250 – 251 global warming and foreign investments, 712 – 713 ice melt ponds, 35 – 36 , 326 , 720 , 735 identifying urban land cover,

365 – 366 , 399 lead levels in mountain moss, 671 mining for dolomite, 167

quantum tunneling, 606 – 607 rockfall rebound length, 57 , 65 , 86 ,

338 , 399 shear strength of rock fractures,

249 – 250 soil loss during rainfall, 718 sound waves from a basketball, 47 , 90 ,

183 , 564 , 594 uranium in Earth’s crust, 230 , 291 water retention of soil cores, 267

Education/school applications:

achievement test scores, 99 blue vs red exam, 77 , 265 bullying behavior study, 445 calories in school lunches, 360 children’s attitude toward reading,

294 children’s use of pronouns, 101 – 102 ,

463 – 464

global warming and foreign investments, 712 – 713 goal congruence in top management teams, 653

goodness-of-fit test with monthly salaries, 757 – 758

hiring executives, 155 IMA salary survey, 344 insurance decision-making, 211 – 212 ,

515 – 516 job tenure, 294 modeling executive salary, 683 – 684 multilevel marketing schemes, 163 museum management, 36 – 37 , 122 , 734 nice guys finish last, 561 – 562 , 569 , 587 ,

593 – 594 occupational safety study, 715 “Pepsi challenge” marketing campaign, 403

predicting hours worked per week,

650 – 651 project team selection, 162 rotary oil rigs running monthly, 540 salary linked to height, 587 self-managed work teams and family life, 467

supervisor-targeted aggression, 679 – 680 used-car warranties, 228 – 229

women in top management, 706 – 707 worker productivity data, 665 – 667 work-life balance, 598

workplace bullying, 671 , 679

Chemicals/chemistry applications:

anthrax detection, 230 anthrax mail room contamination,

215 – 216 arsenic in groundwater, 638 , 645 , 708 arsenic in soil, 601

chemical composition of rainwater,

661 , 671 chemical insect attractant, 172 chemical properties of whole wheat breads, 503

drug content assessment, 425 – 426 insecticide efficacy, 220

mosquito insecticides, 544 mosquito repellents, 717 multiple-sclerosis drug, 755 new insecticide testing, 545 organic chemistry experiment,

605 – 606 pesticides in food samples, 220 roaches and Raid fumigation, 310 scopolamine effect on memory, 505 Teflon-coated cookware hazards, 292 toxic chemical incidents, 132

zinc phosphide in pest control, 103

Computer applications See Electronics/

computer applications Construction/home improvement/home purchases and sales applications:

aluminum siding flaws, 295 assigning workers, 157 , 158 bending strength of wooden roof,

318 , 333 condominium sales, 613 – 614 , 635 – 636 ,

676 – 678 , 700 – 701

Biology/life science applications:

(continued)

insecticide efficacy, 220

Japanese beetle growth, 629

killing insects with low oxygen, 387 , 446

lead levels in mountain moss, 671

mercury levels in wading birds, 361

Mongolian desert ants, 59 – 60 , 91 , 183 ,

379 – 380 , 400 , 427 , 458 , 562 – 563 ,

570 , 594

mortality of predatory birds, 605 – 606

mosquito insecticides, 544

mosquito larvae density, 714

mussel settlement patterns on algae,

533 – 534

new insecticide testing, 545

oil spill impact on seabirds, 102 , 171 ,

461 – 462

parrot fish weights, 404

pig castration, 426 – 427

radioactive lichen, 47 , 58 , 318 , 332 , 379

rainfall and desert ants, 318

ranging behavior of Spanish cattle, 545

rat-in-maze experiment, 67 – 68

rhino population, 35

roaches and Raid fumigation, 310

sea urchins’ feeding habits, 466 – 467

shell lengths of sea turtles, 65 , 243 ,

291 , 308 , 317 , 338

short-day traits of lemmings, 544 – 545

snail mating habits, 463

snow geese feeding habits, 587 , 595 ,

715 – 716

species abundance, 686 , 720

stress in cows prior to slaughter,

517 – 518

subarctic plant study, 749

supercooling temperature of frogs, 294

swim maze study of rat pups, 465

tree frog study, 655

water hyacinth control, 188 , 194

weights of contaminated fish, 264

weight variation in mice, 452 – 454

whale sightings, 209 – 210

whistling dolphins, 100 – 101

zoo animal training, 59 , 344

Business applications:

absentee rates at a jeans plant, 518 – 519

accountants and Machiavellian traits,

365 , 539 – 540

accounting system audit, 154

assertiveness and leadership, 653

auditor’s judgment, factors

affecting, 645

best-paid CEOs, 36 , 100

brokerage analyst forecasts, 132

buy-side vs sell-side analysts’

earnings, 372

consumer sentiment on state of

economy, 322 – 323

deferred tax allowance study, 715

employee performance ratings, 243

entry-level job preferences, 719 – 720

executives who cheat at golf, 136

expected value of insurance, 191

flavor names impact on consumer

choice, 536 – 537

gender and starting salary comparisons,

433 – 435

testing electronic circuits, 465 – 466 time to solve math programming problem, 343 , 403

top Internet search engines, 756 trajectory of electrical circuit, 264 transmission delays in wireless tech-nology, 243

Twitter and wireless Internet, 146 visual attention of video game players,

425 , 450 , 534 wear-out failure time display panels, 266

Entertainment applications See also

Gambling applications

chess player ratings, 99 coin toss, 111 – 112 , 115 , 127 – 131 , 156 ,

176 , 184 craps game outcomes, 185 – 186 dart-throwing, 265

die toss, 114 – 115 , 124 , 131 , 141 – 142 ,

170 game show “Monty Hall Dilemma” choices, 176 , 750 – 751

game show “Showcase Showdown,”

195 life expectancy of Oscar winners, 463 Lotto winning odds, 109 , 119 , 130 ,

144 – 145 , 195 “name game,” 496 , 565 – 566 , 578 ,

587 – 588 , 595 newspaper reviews of movies, 118 Odd Man Out game, 176

parlay card betting, 195 perfect bridge hand, 176 rating funny cartoons, 717 revenues of popular movies, 654 scary-movie study, 343

Scrabble game analysis, 736 “winner’s curse” in auction bidding,

462 – 463

Environmental applications:

air-pollution standards for engines,

375 – 376 aluminum cans contaminated by fire,

332 – 333 ammonia in car exhaust, 100 arsenic in groundwater, 638 , 645 , 708 arsenic in soil, 601 , 630

beach erosion hot spots, 171 – 172 , 194 carbon monoxide content in ciga-rettes, 704 – 705

chemical composition of rainwater, 661 contaminated fish, 167 , 541 , 630 – 631 ,

638 , 708 – 709 contaminated river study, 10 – 11 dissolved organic compound

in lakes, 366 drinking water quality, 19 fecal pollution, 295 – 296 glacial drifts, 86 glass as waste encapsulant, 680 – 681 global warming and foreign invest-ments, 712 – 713

groundwater contamination in wells,

37 , 99 , 122 hazardous material safety data sheets, 344 hazardous waste on-site treatment, 216

teacher perceptions of child behavior,

365 , 372 teaching method comparisons, 411 –

420 , 428 – 430 teaching software effectiveness,

423 – 424 teenagers’ use of emoticons in writing,

326 , 386 time taken to solve math program-ming problem, 343 , 403

verbal ability of delinquents, 295 visually impaired students, 265

Elderly/older-person applications:

Alzheimer patients’ homophone confusion, 439

Alzheimer’s detection, 735 – 736 , 748 Alzheimer’s treatment and study,

318 – 319 , 327 , 735 – 736 dementia and leisure activities, 438 personal networks of older adults, 341 wheelchair user study, 173

Electronics/computer applications:

accuracy of software effort estimates,

685 , 708 CD-ROM reliability, 261 cell phone charging, 236 cell phone handoff behavior, 134 , 216 cell phone use by drivers, 342 , 402 computer crimes, 20

defects in semiconductor wafers, 257 estimating fraction of defective cell phones, 330 – 331

flawed Pentium computer chip, 176 identifying organisms using computers, 386

Internet addiction study, 13 intrusion detection systems, 148 ,

164 – 165 , 168 , 361 LAN videoconferencing, 212 laptop use in middle school, 72 leg movements and impedance, 162 microchip purchases, 174

monitoring quality of power ment, 174

noise in laser imaging, 211 paper friction in photocopier,

226 – 227 phishing attacks to email accounts, 49 ,

260 , 290 PIN pad shipments, 34 – 35 repairing a computer system, 168 requests to a Web server, 230 robot device reliability, 231 robot-sensor system configuration, 190 robots trained to behave like ants,

494 , 504 satellite radio in cars, 20 , 325 , 385 , 396 scanning errors at Wal-Mart, 132 , 332 ,

385 – 386 scanning Internet messages, 753 series and parallel systems, 173 – 174 silicon wafer microchip failure times,

655 , 708 software file updates, 250 software reuse, 21 solder joint inspections, 405 teaching software effectiveness,

423 – 424

cognitive skills for successful arguing,

426 , 457 – 458

college application, 18

college entrance exam scores, 239

college protests of labor exploitation,

48 , 90 , 603

delinquent children, 94 – 95

detection of rigged school milk prices,

428 , 446 , 458

eighth grade math scores, 76

ESL reading ability, 575 – 576 , 594 , 604

ESL students and plagiarism, 122 , 216

exam performance study, 481 , 546

FCAT math test, 264

FCAT scores and poverty, 564 ,

570 , 576

gambling in public high schools, 466

grades in statistics courses, 103

graduate rates of student-athletes, 386

heights of grade-school repeaters, 462 ,

insomnia and education status, 20 , 633

IQ and The Bell Curve, 267 – 268 ,

720 – 721

Japanese reading levels, 34

language impairment in children, 175

laptop use in middle school, 72 , 251 ,

307 , 338

late-emerging reading disabilities, 754

learning from picture book reading,

535 – 536

matching medical students with

residencies, 163

mathematics achievement test, 171

maximum time to take a test, 267

mental maps, 466

online course effectiveness, 360

online courses performance, 586

paper color impact on exam

rating music teachers, 465

reading Japanese books, 46 , 57 – 58 , 65 ,

shock treatment to learners, 139

socialization of graduate students,

716 – 717

standardized test “average,” 103

student gambling on sports, 206

student GPAs, 19 , 77

students’ ability in science, 713

switching majors in college, 464 – 465

xxii A P P L I C AT I O N S I N D E X

sex composition patterns of children

in families, 175 tests for Down syndrome, 167 thrill of a close game, 535 women’s height ranges, 243 – 244

X and Y chromosomes, 189

Health/healthcare applications See also

Medical/medical research/

alternative medicine applications

adolescents with ADHD, 629 – 630 alcohol consumption by college students, 726 , 732 – 733 , 744 Alzheimer’s detection, 735 – 736 , 748 Alzheimer’s treatment and study,

318 – 319 , 327 animal-assisted therapy for heart patients, 73 , 463 , 497 , 505 antismoking campaign, 442 – 443 binge eating therapy, 545 – 546 birth weights of cocaine babies, 400 body fat in men, 257

bulimia study, 425 , 449 – 450 , 457 cancer

lung cancer CT scanning, 20 CDC health survey, 341 childhood obesity study, 652 children’s perceptions of their neigh-borhood, 745

cigarette habit, kicking, 466 cigarette smoking and cancer,

136 – 137 , 357 cyberchondria, 19 , 121 dance/movement therapy, 606 – 607 dementia and leisure activities, 438 dentists’ use of anesthetics, 72 depression treatment, 445 , 480 distress in EMS workers, 713 drinking water quality, 19 drug content assessment, 425 – 426 dust mite allergies, 188 , 194 eating disorders, 48 , 319 emotional distress in firefighters, 681 evaluating health care research reports, 517

frequency of drinking alcohol, 714 healing potential of handling museum objects, 436

health risk to beachgoers, 121 ,

147 , 480 hearing impairment, 754 – 755 heart patients, healing with music, imagery, touch, and prayer, 746 – 747 herbal medicines and therapy, 19 ,

171 , 402 HIV testing and false positives, 167 HIV vaccine efficacy, 750

homophone confusion in Alzheimer patients, 439

honey as a cough remedy, 45 – 46 , 58 ,

65 , 86 , 339 , 458 , 496 , 505 insomnia and education status, 20 insomnia pill, 272 , 288 – 289 iron supplement for anemia, 671 – 672 jaw dysfunction, 734

latex allergy in healthcare workers,

307 , 327 , 365 , 372 , 395 , 399 lipid profiles of hypertensive patients, 307

sweetness of orange juice, 565 , 570 ,

576 – 577 , 594 tomato as a taste modifier, 242 , 291 wine production technologies,

Gambling applications See also

Entertainment applications

casino gaming, 242 – 243 chance of winning at blackjack, 175 chance of winning at craps, 175 – 176 ,

275 – 276 , 281 – 282 Galileo’s passe-dix game, 134 gambling in public high schools, 466 game show “Monty Hall Dilemma”

choices, 176 game show “Showcase Showdown,”

195 jai alai Quinella betting, 122 mathematical theory of partitions, 163 odds of winning a horse race, 175 odds of winning Lotto, 119 , 130 ,

144 – 145 , 195 parlay card betting, 195 roulette, odds of winning at, 172 , 195 straight flush in poker, 164

student gambling on sports, 206

Gardening applications See

Agricultural/gardening/farming applications

734 – 735 gene expression profiling, 132

IQ and The Bell Curve, 267 – 268 ,

720 – 721 light-to-dark transition of genes,

438 – 439 , 519 maize seeds, 173 masculinity and crime, 427 , 747 masculinizing human faces, 403 object recall study, 21

Punnett square for earlobes, 190 quantitative traits in genes, 661 random mutation of cells, 149 reverse-engineering gene identifica-tion, 166 – 167

RNA analysis of wheat genes,

670 – 671

Environmental applications: (continued)

hotel water conservation, 114

ice-melt ponds, 35 – 36 , 326 , 720 , 735

lead in drinking water, 77

lead in metal shredders, 260

lead levels in mountain moss, 671

mussel settlement patterns on algae,

533 – 534

natural-gas pipeline accidents, 149

oil spill impact on seabirds, 102 , 171 ,

removing metal from water, 604

removing soil contaminant, 344

rotary oil rigs running monthly, 540

sedimentary deposits in reservoirs, 266

spreading rate of spilled liquid, 91 ,

566 , 578 , 595

urban counties, factors identifying,

714 – 715

vinyl chloride emissions, 220

water pollution testing, 342

whales entangled in fishing gear, 494 ,

alcoholic fermentation in wine, 439

bacteria in bottled water, 333

baker’s vs brewer’s yeast, 481 , 535

binge eating therapy, 545 – 546

caffeine content of coffee, 333

calories in school lunches, 360

chemical properties of whole wheat

breads, 503

colors of M&Ms candies, 120 – 121

creating menus to influence others,

oven cooking study, 342 – 343

overpriced Starbucks coffee, 325

“Pepsi challenge” marketing

campaign, 403

pesticides in food samples, 220

red snapper served in restaurants,

147 , 326

salmonella in ice cream bars, 344

spoiled wine testing, 216

steak as favorite barbecue food, 445

lot acceptance sampling, 254 – 255 , 261 lot inspection sampling, 216

machine bearings production, 294 machine repair time, 267 , 295 “Made in the USA” survey, 99 ,

342 , 754 metal lathe quality control, 357 nondestructive evaluation, 167 – 168 PIN pad shipments, 34 – 35

pipe wall temperature, 230 preventing production of defective items, 334

preventive maintenance tests, 260 – 261 product failure behavior, 261

quality control monitoring,

254 – 255 , 294 semiconductor material processing, 716 semiconductor wafer defects, 257 soft-drink bottles, 295

soft-drink dispensing machine, 231 solar energy cells, 188 – 189

spall damage in bricks, 609 spare line replacement units, 211 sports news on local TV

broadcasts, 586 strength of fiberboard boxes, 540 surface roughness of pipe, 294 – 295 sweetness of orange juice, 565 , 570 ,

576 – 577 , 594 testing manufacturer’s claim,

287 – 288 thickness of steel sheets, 277 weight deviation in cans, 397 – 398 weight of shipment vs number of bags

of flour, 607 – 608 weights of corn chip bags, 267 yield strength of steel alloy, 686 , 707

Marine/marine life applications:

contaminated fish, 167 , 335 – 337 , 541 ,

630 – 631 , 638 deep-draft vessel casualties, 219 lobster fishing study, 576 , 586 lobster trap placement, 317 , 332 , 338 –

339 , 377 – 378 , 425 marine losses for oil company, 265 mercury levels in wading birds, 361 mussel settlement patterns on algae,

533 – 534 rare underwater sounds, 121 scallop harvesting and the law, 345 sea-ice melt ponds, 720

shell lengths of sea turtles, 65 , 243 ,

291 , 308 , 317 , 338 ship-to-shore transfer times, 261 species abundance, 686

whales entangled in fishing gear, 494 ,

318 – 319 , 327 , 735 – 736 ambulance response time, 148 , 243

credit card lawsuit, 410 , 421 – 422 ,

443 – 444 domestic abuse victims, 206 – 207 , 257 expert testimony in homicide trials of battered women, 662

eyewitnesses and mug shots, 536 ,

745 – 746 federal civil trial appeals, 134 , 189 , 404 forensic analysis of JFK assassination bullets, 168

gangs and homemade weapons, 756 gender discrimination suit, 216 jury trial outcomes, 361 lead bullets as forensic evidence, 123 legal advertising, 598 – 599

lie detector test, 173 masculinity and crime, 427 , 747

No Child Left Behind Act, 104 patent infringement case, 426 , 458 polygraph test error rates, 405 racial profiling by the LAPD, 445

Library/book applications:

learning from picture book reading,

535 – 536 library book checkouts, 86 – 87 library cards, 146

new book reviews, 99 – 100 , 219 reading Japanese books, 46 , 57 – 58 , 65 ,

76 – 77 , 378 , 424 – 425 reading tongue twisters, 463 study of importance of libraries, 33

Life science applications See Biology/

life science applications

Manufacturing applications:

absentee rates at a jeans plant,

518 – 519 accepting or rejecting a shipment, 220 ,

254 – 255 accidents at a plant, 267 aluminum smelter pot life span,

563 , 570 anticorrosive behavior of steel coated with epoxy, 546

boiler drum production, 638 brightness measuring instruments precision, 465

characteristics of lead users, 628 ,

636 – 637 consumer complaints, 138 , 142 contaminated gun cartridges,

189 , 216 cooling method for gas turbines,

366 , 395 , 400 , 632 , 637 – 638 , 645 ,

679 , 709 cutting tool life span tests, 570 ,

595 – 596 cycle availability of a system, 230 defective batteries, 382 – 383 defect rate comparison between machines, 448

flaws in plastic coated wire, 212 freckling of superalloy ingots, 101 glass as a waste encapsulant, 680 – 681 gouges on a spindle, 231

halogen bulb length of life, 261 industrial filling process, 244

low-frequency sound exposure,

542 – 543

major depression and personality

disorders, 661

mandatory new-drug testing, 402

Medicare fraud investigations, 299 ,

315 – 316 , 324 , 331

mental health of a community, 678

MS and exercise, 467

pain empathy and brain activity, 577

pain-relief tablet, testing of, 481 , 536

pain tolerance study, 588

panic disorder treatment, 464

passing physical fitness examination,

physical characteristics of boys, 719

placebo effect and pain, 437

prompting walkers to walk, 542

public perceptions of health

risks, 655

quit-smoking program, 219

sickle-cell anemia, 264 – 265

sleep and mental performance, 446

sleep deprivation study, 402

smoking and resting energy, 670

social interaction of mental

patients, 367

stress and diet, 154

stress and violence, 294

stress reduction with plants, 518

summer weight-loss camp, 436

sun safety, 717 – 718

tendon pain treatment, 480 – 481 , 516

tests for Down syndrome, 167

virtual-reality–based rehabilitation

systems, 534 – 535

virtual reality hypnosis for pain, 361

vitamin-B supplement, 544

waking sleepers early, 319

weight loss diets, 411 – 415

wheelchair control, 166

Home improvement See Construction/

home improvement/home

pur-chases and sales applications

Home maintenance applications:

aluminum siding flaws, 295

burglary risk in cul-de-sacs, 332

dye discharged in paint, 267

home improvement grants, 216

portable grill displays selection, 122 ,

162 , 189 , 404 – 405

ranking detergents, 159 – 160

roaches and Raid fumigation, 310

Home purchases and sales applications

See Construction/home

improve-ment/home purchases and sales

applications

Legal/legislative applications:

camera that detects liars, 360

child abuse report, 644

community responses to violent

crime, 662 – 663

xxiv A P P L I C AT I O N S I N D E X

geography journals, cost of,

90 – 91 , 605 glass as a waste encapsulant, 680 – 681 hotel guest satisfaction, 206 , 257 Hot Tamale caper, 405

Howard Stern on Sirius radio, 15 – 16 ideal height of one’s mate, 565 , 570 ,

576 , 594 – 595 identical twins reared apart, 464 identifying target parameter, 465 jail suicide risk, 103 , 104 , 344 jitter in water power system, 338 , 400 laughter among deaf signers, 437 , 450 left-handed tasks, perform of, 543 lie detector test, 173

listening ability of infants, 519 lobster trap placement, 317 luck, 541 – 542

luggage inspection at Newark airport,

257 maintenance support system selection, 163

married-women study, 219 matching socks, 123 microwave oven length of life,

259 – 260 mosquito repellents, 717 most powerful women in America, 57 ,

66 , 71 , 86 , 242 , 249 , 494 – 495 multilevel marketing schemes, 163 National Bridge Inventory, 20 National Firearms Survey, 146 ,

325 – 326 normal curve approximation, 266 – 267 number of tissues in box, 350 , 359 ,

371 , 384 one-shot devices, 221 pennies, dates of, 98 perfect bridge hand, 176 planning-habits survey, 445 Post Office violence, 171 predicting electrical usage, 646 – 649 ,

689 – 691 project team selections, 162 psychic ability, 149 , 207 quantitative models of music, 562 , 570 questionnaire mailings, 220

random-digit telephone dialing, 153 randomization in studying TV com-mercials, 162 – 163

randomly sampling households,

151 – 152 random numbers, 230 ranking detergents, 159 – 161 ratings of five-star hotels, 462 reality TV and cosmetic surgery, 631 ,

637 , 644 , 669 , 680 recall of TV commercials, 495 , 504 ,

661 – 662 regression through the origin,

607 – 608 retailer interest in shopping behavior,

643 revenues of popular movies, 654 salary linked to height, 587 selecting a random sample of students, 171

selecting soldiers for dangerous missions, 118 , 156 , 158

passing physical fitness examination,

197 – 201 patients’ length of stay in hospital,

88 – 89 , 370 post-op nausea study, 123 psoriasis, treatment of with the

“Doctorfish of Kangal,” 85 scopolamine effect on memory, 505 sickle-cell anemia, 264 – 265 skin cancer treatment, 192 – 193 sleep apnea and sleep stage transi-tioning, 132 – 133 , 147

smoking and resting energy, 670 splinting in mountain-climbing acci-dents, 326 – 327

Test of Knowledge about Epilepsy (KAE), 292

tests for Down syndrome, 167 virtual reality hypnosis for pain, 361 visual search and memory study,

437 – 438

Miscellaneous applications:

acquiring a pet, 205 , 256 ages of cable TV shoppers, 373 ages of TV viewers, 7

air bag danger to children, 344 – 345 Benford’s Law of Numbers, 102 , 219 body image dissatisfaction, 26 , 44 ,

70 – 71 , 84 bottled water analysis, 206 bottled water comparisons, 482 box plots and standard normal distribution, 244

brown-bag lunches at work, 343 burglary risk in cul-de-sacs, 332 bus rapid-transit study, 686 cable-TV home shoppers, 450 census sampling, 153 – 154 children’s recall of TV ads, 424 , 457 clock auction price, 617 – 618 , 620 – 624 ,

626 – 627 , 634 – 635 , 640 – 642 cocaine sting, 180 , 204 , 214 – 215 community responses to violent crime, 662 – 663

contaminated gun cartridges, 189 , 216 conversing with hearing impaired, 607 cooling method for gas turbines, 366 ,

395 , 400 , 632 , 637 – 638 , 645 , 679 ,

709 countries allowing a free press, 220 coupon usage, 757

cracks in highway pavement, 404 Crime Watch neighborhood, 220 customer arrivals at a bakery, 212 customers in line at a Subway shop,

183 cycle availability of a system, 230 Davy Crockett’s use of words, 212 deep-draft vessel casualties, 219 deferred tax allowance study, 715 dowsers for water detection, 550 ,

559 – 560 , 574 – 575 , 584 – 585 dust mite allergies, 188 , 194 elevator waiting times, 265 eye and head movement relationship,

577 – 578 Florida license plates, 163

Medical/medical research/alternative

medicine applications: (continued )

angioplasty’s benefits challenged,

445 – 446 , 450

animal-assisted therapy for heart

patients, 73 , 463 , 497 , 505

arrival times of hospital patients, 265

asthma drug study, 333

birth weights of cocaine babies, 400

blood typing method, 90

brain disease genetics, 714

brain specimen research, 48 , 86 , 343

childhood obesity study, 652

clinical trials involving humans, 152 – 153

drug content assessment, 250

drug designed to reduce blood loss,

29 – 31

drug response time, 362 – 364 ,

368 – 369 , 556 – 559 , 568 – 569 ,

572 – 574 , 584

eating disorder research, 48 , 290 – 291

emergency arrivals, length of time

between, 258

emergency room bed availability, 221

emergency room waiting time, 257

epilepsy, attitudes toward, 292

errors in medical tests, 403

ethnicity and pain perception, 428

eye fixation experiment, 211

eye movement and spatial distortion,

644

eye refractive study, 60

gestation time for pregnant women,

265 – 266

gummy bears red or yellow and

flavors, 386

hand-washing vs hand-rubbing, 72 , 292

heart rate during laughter, 365

herbal medicines and therapy, 19 , 171 ,

402

HIV testing and false positives, 167

HIV vaccine efficacy, 750

homophone confusion in Alzheimer

patients, 439

hospital admissions study, 128 – 129

insomnia pill, 272 , 288 – 289

interocular eye pressure, 405

iron supplement for anemia, 671 – 672

jaw dysfunction, 734

LASIK surgery complications, 256

lung cancer CT scanning, 20

male fetal deaths following 9/11/2001,

387

mandatory new-drug testing, 402

Medicare fraud investigations, 299 ,

315 – 316 , 324 , 331

melanoma deaths, 256

MS and exercise, 467

new blood typing method, 562 , 569 – 570

olfactory reference syndrome (ORS),

326 , 333

organ transplant matching, 175

pain empathy and brain activity, 577

pain tolerance study, 588

Religion applications:

belief in an afterlife, 220

Do you believe in the Bible? survey,

36 – 37 , 326 , 736 marital status and religion, 741 – 743 membership in new religious move-ment, 654 – 655

political representation of religious groups, 736

politics and religion, 754 religious symbolism in TV commercials, 446

Safety applications:

hazardous material safety data sheets, 344 occupational safety study, 715 sun safety, 717 – 718

School applications See Education/

school applications Sociological applications:

fieldwork methods, 36 , 121 , 735 genealogy research, 35 Hite Report, 103 – 104 impact of race on football card values,

661 , 669 – 670 socialization of graduate students,

716 – 717

Space science applications See

Astronomy/space science applications

Sports/exercise/fitness applications:

altitude effects on climbers, 462 baseball batting averages vs wins,

602 – 603 baseball elevation effect on hitting performance, 89 , 578

baseball hitting averages, 251 baseball runs scored, 629 , 637 basketball NBA draft lottery,

149 – 150 basketball shooting free throws, 173 basketball stacking in the NBA, 172 basketball team choice, 162 boxing, massage vs rest, 20 , 518 , 577 college tennis recruiting with Web site, 494 , 503

drug testing in athletes, 167 drug tests of Olympic athletes, effec-tiveness of, 446

executive workout dropouts, 462 exercise workout dropouts, 343 football fourth down tactics, 586 ,

652 – 653 football speed training, 309 , 326 , 333 football “topsy-turvy” seasons in college football, 516

golf ball brand comparisons, 478 , 479 ,

486 – 488 , 499 – 500 , 509 – 512 , 524 – 528 golf ball specifications, 207

golfers’ driving performance, 59 , 91 ,

251 , 563 – 564 , 576 , 594 golf Ryder Cup tournament, 189 golf USGA golf ball tests, 333 location of major sports venues, 753 long-jump takeoff error, 608

verifying voter petitions, 405 voting for mayor, 202 – 203 voting in primary elections, 206 winning a war, 148

Psychological applications See also

Behavioral study applications

alcohol, threats, and electric shocks,

244 alcohol and marriage study, 540 – 541 appraisals and negative emotions,

146 assertiveness and leadership, 653 attention time given to twins, 309 body orientation study, 757 bulimia study, 425

children’s perceptions of their neighborhood, 745

choosing a mother, 21 cognitive impairment of schizophren-ics, 424 , 458

dating and disclosure, 21 distress in EMS workers, 713 divorced couples study, 143 – 144 dream experiment, 174

effectiveness of TV program on marijuana use, 730 – 731 emotional distress in firefighters,

681 emotional empathy in young adults,

372 facial expression study, 543 – 544 guilt in decision making, 20 – 21 , 133 ,

146 , 216 , 503 , 749 Internet addiction study, 13

IQ and mental deficiency, 747 – 748 listen-and-look study, 713 – 714 major depression and personality disorders, 661

mental health of a community, 678 object recall study, 21

panic disorder treatment, 464 personalities of cocaine abusers,

464 personality and aggressive behavior,

308 , 707 pitch memory of amusiacs, 318 , 333 ,

379 post-traumatic stress of POWs, 404 rating funny cartoons, 717

rotating objects, view of, 587 shock treatment to learners, 139 sleep deprivation study, 402 stimulus reaction study, 81 – 82 supervisor-targeted aggression,

679 – 680 susceptibility to hypnosis,

13 – 14 , 294 , 749 task deviations, 42 – 43 tip-of-the-tongue study, 446 unconscious self-esteem study,

644 – 645 verbal ability of delinquents, 295 violence and stress, 294

violent behavior in children, 653 violent song lyrics and aggression,

154 , 536 waiting in line, 643 – 644 workplace bullying, 671 , 679

Showcase Showdown (Price is Right),

195

single-parent families, 386

skin cream effectiveness, 386 – 387

social network densities, 230

social networking sites in the UK, 132

social network usage, 2 , 17

solar energy cells, 188 – 189

spanking, parents who condone, 219 ,

sterile couples in Jordan, 171

surface roughness of pipe, 294 – 295

susceptibility to hypnosis, 13 – 14 , 294 ,

749

swim maze study, 465

symmetric vs skewed data sets, 59

U.S Zip codes, 162

victims of violent crime, 323 – 324

walking in circles when lost, 380

weapons development, 225 , 240 – 241 ,

247 – 248

welfare workers study, 139 – 140

when sick at home, 326

Winchester bullet velocity, 72

wind turbine blade stress, 562 , 586

Motor vehicle applications See

Automotive/motor vehicle

applications

Nuclear applications:

active nuclear power plants, 60 , 66

nuclear missile housing parts, defects

countries allowing a free press, 220

electoral college votes, 244

exit polls, 176

Iraq War survey, 755

order-to-delivery times in war zones,

selecting committee members, 173

U.S Treasury deficit prior to Civil

War, 19

xxvi A P P L I C AT I O N S I N D E X

traveling between cities, 162 travel management professional salaries, 293 – 294

unleaded fuel costs, 291 vacation destination, 630

Weather applications:

California rain levels, 638 chance of rainfall, 121 chemical composition of rainwater,

661 , 671 quantum tunneling, 606 – 607 rainfall and desert ants, 318 rainfall estimation, 604 – 605 soil loss during rainfall, 718 Texas droughts, 186 tropical island temperatures, 265

goal target, 243 sports news on local TV broadcasts,

586 sports participation survey, 607 sprint speed training, 19 student gambling on sports, 206 thrill of a close game, 535 volleyball positions, 163 – 164

Travel applications:

bus rapid-transit study, 686 cracks in highway pavement, 404 cruise ship sanitation inspection, 47 ,

71 , 76 , 86 , 251 driving routes, 156 hotel guest satisfaction, 206 , 257 ship-to-shore transfer times, 261

Sports/exercise/fitness applications:

(continued )

marathon winning times, 606 , 670

massage, effect of on boxers,

518 , 577 , 588

mile run times, 266

odds of winning a horse race, 175

parents’ behavior at a gym meet, 220

Play Golf America program, 360

point spreads of NFL games,

399 – 400

professional athlete salaries, 103

prompting walkers to walk, 542

scouting an NFL free agent, 450

shuffleboard, seeded players at

tournament, 148 – 149

soccer

1.1 The Science of Statistics

1.2 Types of Statistical Applications

1.3 Fundamental Elements of Statistics

1.4 Types of Data

1.5 Collecting Data

1.6 The Role of Statistics in Critical

Thinking and Ethics

Where We’re Going

• Introduce the field of statistics (1.1)

• Demonstrate how statistics applies to real-world problems (1.2)

• Introduce the language of statistics and the key elements to any statistical problem (1.3)

• Differentiate between population and sample data (1.3)

• Differentiate between descriptive and inferential statistics (1.3)

• Identify the different types of data and data collection methods (1.4–1.5)

• Discover how critical thinking through statistics can help improve our quantitative literacy (1.6)

The Pew Research Center, a nonpartisan organization funded

by a Philadelphia-based charity, has conducted over 100

sur-veys on Internet usage in the United States as part of the Pew

Internet & American Life Project (PIALP) The PIALP has

recently published a series of reports on teens and adults from

ages 18 to 29 years—called the “Millennial Generation.” In

a 2010 report titled “Social Media & Mobile Internet Use,”

the PIALP examined the Millennial Generation’s attitudes

and behavior towards online social networks (e.g., Facebook,

MySpace, Twitter) According to Wikipedia (the free online

encyclopedia), social media are “media for social interaction,

using highly accessible and scalable publishing techniques”

such as Weblogs, Internet forums, Twitter, and social

net-working sites such as Facebook and MySpace

Results from several of the survey questions asked of the

teens are provided here:

• Internet Use

When asked how often they use the Internet, teens

responded:

Several times a day 36%

About once a day 27%

When asked if they use social network sites like Facebook

or MySpace, teens responded:

Yes 73%

No 27%

• TwitterWhen asked if they use Twitter, teens responded:

Yes 8%

No 91%

• Text MessagingWhen asked how often they send text messages on their cell phones, teens responded:

Several times per week 10%

At least once a week 5%

Less than once a week 3%

• Average Number of Phone Calls

On an average day, teens make and receive 10.7 phone calls on their cell phones

• Average Number of Text Messages

On an average day, teens send and receive 112.4 text messages on their cell phones

In the following Statistics in Action Revisited sections,

we discuss several key statistical concepts covered in this chapter that are relevant to the Pew Internet & American Life Project survey

• Identifying the Population, Sample, and Inference (p 9)

• Identifying the Data Collection Method and Data Type (p 14)

• Critically Assessing the Ethics of a Statistical Study (p 17)Based on Pew Research Center for the People & the Press, “Social Media

& Mobile Internet Use” report © 2010 PIALF

Statistics IN Action Social Media Networks and

the Millennial Generation

1.1 The Science of Statistics

What does statistics mean to you? Does it bring to mind batting averages, Gallup polls, unemployment figures, or numerical distortions of facts (lying with statistics!)? Or is

it simply a college requirement you have to complete? We hope to persuade you that statistics is a meaningful, useful science whose broad scope of applications to business, government, and the physical and social sciences is almost limitless We also want to show that statistics can lie only when they are misapplied Finally, we wish to demon- strate the key role statistics plays in critical thinking—whether in the classroom, on the job, or in everyday life Our objective is to leave you with the impression that the time you spend studying this subject will repay you in many ways

The Random House College Dictionary defines statistics as “the science that deals

with the collection, classification, analysis, and interpretation of information or data.” Thus, a statistician isn’t just someone who calculates batting averages at baseball games

or tabulates the results of a Gallup poll Professional statisticians are trained in statistical

science That is, they are trained in collecting information in the form of data , evaluating

the information, and drawing conclusions from it Furthermore, statisticians determine what information is relevant in a given problem and whether the conclusions drawn from a study are to be trusted

1.2 Types of Statistical Applications

“Statistics” means “numerical descriptions” to most people Monthly housing starts, the failure rate of liver transplants, and the proportion of African-Americans who feel brutalized by local police all represent statistical descriptions of large sets of data col- lected on some phenomenon (Later, in Section 1.4, we learn that not all data is numeri- cal in nature.) Often the data are selected from some larger set of data whose charac-

teristics we wish to estimate We call this selection process sampling For example, you

might collect the ages of a sample of customers who shop for a particular product online

to estimate the average age of all customers who shop online for the product Then you

could use your estimate to target the Web site’s advertisements to the appropriate age group Notice that statistics involves two different processes: (1) describing sets of data and (2) drawing conclusions (making estimates, decisions, predictions, etc.) about the sets of data on the basis of sampling So, the applications of statistics can be divided into

two broad areas: descriptive statistics and inferential statistics

Statistics is the science of data This involves collecting, classifying, summarizing,

organizing, analyzing, presenting, and interpreting numerical information

In the next section, you’ll see several real-life examples of statistical applications that involve making decisions and drawing conclusions

BIOGRAPHY FLORENCE NIGHTINGALE (1820–1910)

The Passionate Statistician

In Victorian England, the “Lady of the Lamp” had a mission to improve the squalid field hospital conditions of the British army during the Crimean War Today, most historians consider Florence Nightingale to be the founder of the nursing profession To convince members of the British Parliament of the need for supplying nursing and medical care to soldiers in the field, Nightingale compiled massive amounts of data from army files Through a remarkable series of graphs (which included the first pie chart), she demonstrated that most of the deaths in the war either were due to illnesses contracted outside the battlefield or occurred long after battle action from wounds that went untreated Florence Nightingale’s compassion and self-sacrificing nature, coupled with her ability to collect, arrange, and present large amounts of data, led some to call her the Passionate Statistician

Descriptive statistics utilizes numerical and graphical methods to look for patterns

in a data set, to summarize the information revealed in a data set, and to present that information in a convenient form

Inferential statistics utilizes sample data to make estimates, decisions, predictions,

or other generalizations about a larger set of data

Although we’ll discuss both descriptive and inferential Statistics in the chapters

that follow, the primary theme of the text is inference

Let’s begin by examining some studies that illustrate applications of statistics

Study 1.1 “Best-Selling Girl Scout Cookies” ( www.girlscouts.org )

Since 1917, the Girl Scouts of America have been selling boxes of cookies Currently, there are eight varieties for sale: Thin Mints, Samoas, Caramel DeLites, Tagalongs,

C H A P T E R 1 Statistics, Data, and Statistical Thinking

4

Study 1.2 “Does Playing Video Games Make for Better Visual Attention Skills?”

(Journal of Articles in Support of the Null Hypothesis , Vol 6, No 1, 2009)

Researchers at Griffin University (Australia) conducted a study to determine whether video game players have superior visual attention skills compared to non–video game players Each in a sample of 65 male psychology students was classified as a video game player or a nonplayer The two groups were then subjected to a series of visual attention tasks that included the “attentional blink” test, the “field of view” test, and the “repeti- tion blindness” test Except for attentional blink, no differences in the performance of the two groups were found From this analysis, the researchers inferred “a limited role for video game playing in the modification of visual attention.” Thus, inferential statis- tics was applied to arrive at this conclusion

Study 1.3 “Animal Assisted Therapy … [for] Hospitalized Heart Failure Patients”

(American Heart Association Conference , November 2005)

A team from the UCLA Medical Center and School of Nursing, led by RN Kathie Cole, conducted a study to gauge whether animal-assisted therapy can improve the physiological responses of heart failure patients Cole and her colleagues studied

76 heart patients, randomly divided into three groups Each person in one group

of patients was visited by a human volunteer accompanied by a trained dog, each person in another group was visited by a volunteer only, and the third group was not visited at all The researchers measured patients’ physiological responses (lev- els of anxiety, stress, and blood pressure) before and after the visits An analysis of the data revealed that those patients with animal-assisted therapy had significantly greater drops in levels of anxiety, stress, and blood pressure Thus, the research- ers concluded that “pet therapy has the potential to be an effective treatment … for patients hospitalized with heart failure.” Like Study 1.2, this study is an example of the use of inferential statistics The medical researchers used data from 76 patients

to make inferences about the effectiveness of animal-assisted therapy for all heart failure patients

These studies provide three real-life examples of the uses of statistics Notice that each involves an analysis of data, either for the purpose of describing the data set (Study 1.1) or for making inferences about a data set (Studies 1.2 and 1.3)

Figure 1.1

MINITAB graph of best-selling

Girl Scout cookies

For example, populations may include (1) all employed workers in the United States, (2) all registered voters in California, (3) everyone who is afflicted with AIDS, (4) all the cars produced last year by a particular assembly line, (5) the entire stock of spare parts available at United Airlines’ maintenance facility, (6) all sales made at the drive-in window of a McDonald’s restaurant during a given year, or (7) the set of all accidents

occurring on a particular stretch of interstate highway during a holiday period Notice that the first three population examples 11-32 are sets (groups) of people, the next two 14-52 are sets of objects, the next (6) is a set of transactions, and the last (7) is a set of

events Notice also that each set includes all the units in the population

In studying a population, we focus on one or more characteristics or properties

of the units in the population We call such characteristics variables For example, we

may be interested in the variables age, gender, and number of years of education of the people currently unemployed in the United States

1.3 Fundamental Elements of Statistics

Statistical methods are particularly useful for studying, analyzing, and learning about

populations of experimental units

An experimental (or observational) unit is an object (e.g., person, thing,

transac-tion, or event) about which we collect data

A population is a set of units (usually people, objects, transactions, or events) that

we are interested in studying

A variable is a characteristic or property of an individual experimental (or

observa-tional) unit in the population

The name variable is derived from the fact that any particular characteristic may

vary among the units in a population

In studying a particular variable, it is helpful to be able to obtain a numerical resentation for it Often, however, numerical representations are not readily available,

rep-so measurement plays an important supporting role in statistical studies Measurement

is the process we use to assign numbers to variables of individual population units We might, for instance, measure the performance of the president by asking a registered voter to rate it on a scale from 1 to 10 Or we might measure the age of the U.S work- force simply by asking each worker, “How old are you?” In other cases, measurement involves the use of instruments such as stopwatches, scales, and calipers

If the population you wish to study is small, it is possible to measure a variable for every unit in the population For example, if you are measuring the GPA for all incoming first-year students at your university, it is at least feasible to obtain every GPA When we

measure a variable for every unit of a population, it is called a census of the population

Typically, however, the populations of interest in most applications are much larger, involving perhaps many thousands, or even an infinite number, of units Examples of large populations are those following the definition of population above, as well as all graduates of your university or college, all potential buyers of a new iPhone, and all pieces of first-class mail handled by the U.S Post Office For such populations, conduct- ing a census would be prohibitively time consuming or costly A reasonable alternative

would be to select and study a subset (or portion) of the units in the population

A sample is a subset of the units of a population

C H A P T E R 1 Statistics, Data, and Statistical Thinking

6

For example, instead of polling all 145 million registered voters in the United States during a presidential election year, a pollster might select and question a sample of just 1,500 voters (See Figure 1.2 .) If he is interested in the variable “presidential preference,”

he would record (measure) the preference of each vote sampled

145 millionvoter ID cards

1,500th voter ID cardselected

2nd voter ID cardselected

1st voter ID cardselected

Figure 1.2

A sample of voter registration

cards for all registered voters

* The terms population and sample are often used to refer to the sets of measurements themselves, as well as

to the units on which the measurements are made When a single variable of interest is being measured, this usage causes little confusion But when the terminology is ambiguous, we’ll refer to the measurements as

population data sets and sample data sets , respectively

A statistical inference is an estimate, prediction, or some other generalization about

a population based on information contained in a sample

That is, we use the information contained in the smaller sample to learn about the larger

population * Thus, from the sample of 1,500 voters, the pollster may estimate the

per-centage of all the voters who would vote for each presidential candidate if the election were held on the day the poll was conducted, or he might use the results to predict the outcome on election day

After the variables of interest for every unit in the sample (or population) are measured, the data are analyzed, either by descriptive or inferential statistical methods

The pollster, for example, may be interested only in describing the voting patterns of the

sample of 1,500 voters More likely, however, he will want to use the information in the sample to make inferences about the population of all 145 million voters

a Describe the population

b Describe the variable of interest

c Describe the sample

d Describe the inference

Solution

a Since we are interested in the responses of cola consumers in a taste test, a cola

consumer is the experimental unit Thus, the population of interest is the collection

or set of all cola consumers

b The characteristic that Pepsi wants to measure is the consumer’s cola preference, as

revealed under the conditions of a blind taste test, so cola preference is the variable

of interest

c The sample is the 1,000 cola consumers selected from the population of all cola

consumers

d The inference of interest is the generalization of the cola preferences of the 1,000

sampled consumers to the population of all cola consumers In particular, the

prefer-ences of the consumers in the sample can be used to estimate the percentages of cola

consumers who prefer each brand

Example 1.1

Key Elements of a

Statistical Problem—

Ages of TV Viewers

Problem According to Variety (Aug 27, 2009), the

aver-age aver-age of viewers of live television programs broadcast

on CBS, NBC, and ABC is 51 years Suppose a rival network (e.g., Fox) executive hypothesizes that the average age of Fox viewers is less than 51 To test her hypothesis, she samples 200 Fox viewers and deter- mines the age of each

a Describe the population

b Describe the variable of interest

c Describe the sample

d Describe the inference

Solution

a The population is the set of units of interest to the TV executive, which is the set of

all Fox viewers

b The age (in years) of each viewer is the variable of interest

c The sample must be a subset of the population In this case, it is the 200 Fox viewers

selected by the executive

d The inference of interest involves the generalization of the information contained in

the sample of 200 viewers to the population of all Fox viewers In particular, the

exec-utive wants to estimate the average age of the viewers in order to determine whether

it is less than 51 years She might accomplish this by calculating the average age of the sample and using that average to estimate the average age of the population

Look Back A key to diagnosing a statistical problem is to identify the data set collected (in this example, the ages of the 200 Fox viewers) as a population or a sample

C H A P T E R 1 Statistics, Data, and Statistical Thinking

8

The preceding definitions and examples identify four of the five elements of an inferential statistical problem: a population, one or more variables of interest, a sample, and an inference But making the inference is only part of the story; we also need to

know its reliability —that is, how good the inference is The only way we can be certain

that an inference about a population is correct is to include the entire population in our

sample However, because of resource constraints (i.e., insufficient time or money), we

usually can’t work with whole populations, so we base our inferences on just a portion

of the population (a sample) Thus, we introduce an element of uncertainty into our

inferences Consequently, whenever possible, it is important to determine and report the reliability of each inference made Reliability, then, is the fifth element of inferential statistical problems

The measure of reliability that accompanies an inference separates the science of

statistics from the art of fortune-telling A palm reader, like a statistician, may examine

a sample (your hand) and make inferences about the population (your life) However, unlike statistical inferences, the palm reader’s inferences include no measure of reliability

Suppose, like the TV executive in Example 1.1 , we are interested in the error of

estimation (i.e., the difference between the average age of a population of TV viewers

and the average age of a sample of viewers) Using statistical methods, we can

deter-mine a bound on the estimation error This bound is simply a number that our

estima-tion error (the difference between the average age of the sample and the average age

of the population) is not likely to exceed We’ll see in later chapters that this bound is

a measure of the uncertainty of our inference The reliability of statistical inferences is discussed throughout this text For now, we simply want you to realize that an inference

is incomplete without a measure of its reliability

Look Back In determining whether the study is inferential or descriptive, we assess whether Pepsi is interested in the responses of only the 1,000 sampled customers (descriptive statistics) or in the responses of the entire population of consumers (infer- ential statistics)

Now Work Exercise 1.14b

A measure of reliability is a statement (usually quantitative) about the degree of

uncertainty associated with a statistical inference

Four Elements of Descriptive Statistical Problems

1 The population or sample of interest

2 One or more variables (characteristics of the population or sample units) that

are to be investigated

3 Tables, graphs, or numerical summary tools

4 Identification of patterns in the data

Let’s conclude this section with a summary of the elements of descriptive and of inferential statistical problems and an example to illustrate a measure of reliability

Five Elements of Inferential Statistical Problems

1 The population of interest

2 One or more variables (characteristics of the population units) that are to be

investigated

3 The sample of population units

4 The inference about the population based on information contained in the sample

5 A measure of the reliability of the inference

indi-of all cola consumers in the Pepsi bottler’s marketing region could be measured

Solution When the preferences of 1,000 consumers are used to estimate those of all consumers in a region, the estimate will not exactly mirror the preferences of the population For example, if the taste test shows that 56% of the 1,000 cola consumers preferred Pepsi, it does not follow (nor is it likely) that exactly 56% of all cola drinkers

in the region prefer Pepsi Nevertheless, we can use sound statistical reasoning (which we’ll explore later in the text) to ensure that the sampling procedure will generate esti- mates that are almost certainly within a specified limit of the true percentage of all cola consumers who prefer Pepsi For example, such reasoning might assure us that the esti- mate of the preference for Pepsi is almost certainly within 5% of the preference of the population The implication is that the actual preference for Pepsi is between 51% [i.e.,

156 - 52 %] and 61% [i.e., 156 + 52 %]—that is, 156 { 52 % This interval represents

a measure of the reliability of the inference

Look Ahead The interval 56 { 5 is called a confidence interval , since we are confident

that the true percentage of cola consumers who prefer Pepsi in a taste test falls into the range (51, 61) In Chapter 7 , we learn how to assess the degree of confidence (e.g., a 90%

or 95% level of confidence) in the interval

Consider the 2010 Pew Internet & American Life Project

survey on social networking and cell phone use by teenagers

In particular, consider the survey results on social

network-ing sites like Facebook or MySpace The experimental unit

for the study is a teenager (the person answering the

ques-tion), and the variable measured is the response (“yes” or

“no”) to the question

The Pew Research Center reported that approximately

800 teens participated in the study Obviously, that number

is not all of the teenagers in the United States Consequently,

the 800 responses represent a sample selected from the much

larger population of all American teenagers

Earlier surveys found that 55% of American

teenag-ers used an online social networking site in 2006, and 65%

Identifying the Population, Sample, and Inference

in 2008 These are descriptive statistics that provide information on the popu-larity of social networking in past years Since 73% of the surveyed teens in 2010 used an online social networking site, the Pew Research Center inferred that teens’ usage of social networking cites continues its upward trend, with more and more teens getting online each year That

is, the researchers used the tive statistics from the sample to make

descrip-an inference about the current population of Americdescrip-an agers’ use of social networking

Statistics IN Action Revisited

You have learned that statistics is the science of data and that data are obtained by suring the values of one or more variables on the units in the sample (or population) All data (and hence the variables we measure) can be classified as one of two general types:

mea-quantitative data and qualitative data

Quantitative data are data that are measured on a naturally occurring numerical scale * The following are examples of quantitative data:

1 The temperature (in degrees Celsius) at which each piece in a sample of

20 pieces of heat-resistant plastic begins to melt

* Quantitative data can be subclassified as either interval data or ratio data For ratio data, the origin

(i.e., the value 0) is a meaningful number But the origin has no meaning with interval data Consequently,

we can add and subtract interval data, but we can’t multiply and divide them Of the four quantitative data sets listed as examples, (1) and (3) are interval data while (2) and (4) are ratio data

C H A P T E R 1 Statistics, Data, and Statistical Thinking

10

2 The current unemployment rate (measured as a percentage) in each of the 50

states

3 The scores of a sample of 150 law school applicants on the LSAT, a

standard-ized law school entrance exam administered nationwide

4 The number of convicted murderers who receive the death penalty each year

over a 10-year period

* Qualitative data can be subclassified as either nominal data or ordinal data The categories of an ordinal

data set can be ranked or meaningfully ordered, but the categories of a nominal data set can’t be ordered

Of the four qualitative data sets listed as examples, (1) and (2) are nominal and (3) and (4) are ordinal

Quantitative data are measurements that are recorded on a naturally occurring

numerical scale

Qualitative (or categorical ) data are measurements that cannot be measured on a

natural numerical scale; they can only be classified into one of a group of categories

In contrast, qualitative data cannot be measured on a natural numerical scale; they can only be classified into categories * (For this reason, this type of data is also called

categorical data ) Examples of qualitative data include the following:

1 The political party affiliation (Democrat, Republican, or Independent) in a

sample of 50 voters

2 The defective status (defective or not) of each of 100 computer chips

manufac-tured by Intel

3 The size of a car (subcompact, compact, midsize, or full size) rented by each of

a sample of 30 business travelers

4 A taste tester’s ranking (best, worst, etc.) of four brands of barbecue sauce for

a panel of 10 testers Often, we assign arbitrary numerical values to qualitative data for ease of com- puter entry and analysis But these assigned numerical values are simply codes: They cannot be meaningfully added, subtracted, multiplied, or divided For example, we might code Democrat = 1, Republican = 2, and Independent = 3 Similarly, a taste tester might rank the barbecue sauces from 1 (best) to 4 (worst) These are simply arbi- trarily selected numerical codes for the categories and have no utility beyond that

1 River/creek where each fish was captured

2 Species (channel catfish, largemouth bass, or smallmouth buffalo fish)

3 Length (centimeters)

4 Weight (grams)

5 DDT concentration (parts per million)

We demonstrate many useful methods for analyzing quantitative and qualitative data

in the remaining chapters of the text But first, we discuss some important ideas on data collection in the next section

(For future analyses, these data are saved in the FISHDDT file.) Classify each of

the five variables measured as quantitative or qualitative

Look Ahead It is essential that you understand whether the data you are interested in are quantitative or qualitative, since the statistical method appropriate for describing, reporting, and analyzing the data depends on the data type (quantitative or qualitative)

Now Work Exercise 1.12

1.5 Collecting Data

Once you decide on the type of data—quantitative or qualitative—appropriate for the problem at hand, you’ll need to collect the data Generally, you can obtain data in three different ways:

1 From a published source

2 From a designed experiment

3 From an observational study (e.g., a survey )

Sometimes, the data set of interest has already been collected for you and is

avail-able in a published source , such as a book, journal, or newspaper For example, you may

want to examine and summarize the divorce rates (i.e., number of divorces per 1,000 population) in the 50 states of the United States You can find this data set (as well as

numerous other data sets) at your library in the Statistical Abstract of the United States ,

published annually by the U.S government Similarly, someone who is interested in monthly mortgage applications for new home construction would find this data set in

the Survey of Current Business , another government publication Other examples of published data sources include The Wall Street Journal (financial data) and The Sporting

News (sports information) The Internet (World Wide Web) now provides a medium by

which data from published sources are readily obtained *

A second method of collecting data involves conducting a designed experiment ,

in which the researcher exerts strict control over the units (people, objects, or things) in the study For example, an often-cited medical study investigated the potential of aspi- rin in preventing heart attacks Volunteer physicians were divided into two groups: the

treatment group and the control group Each physician in the treatment group took one

aspirin tablet a day for one year, while each physician in the control group took an rin-free placebo made to look like an aspirin tablet The researchers—not the physicians under study—controlled who received the aspirin (the treatment) and who received the placebo As you’ll learn in Chapter 10 , a properly designed experiment allows you to extract more information from the data than is possible with an uncontrolled study

Finally, observational studies can be employed to collect data In an observational study , the researcher observes the experimental units in their natural setting and records

the variable(s) of interest For example, a child psychologist might observe and record the

* With published data, we often make a distinction between the primary source and a secondary source

If the publisher is the original collector of the data, the source is primary Otherwise, the data is source data

secondary-C H A P T E R 1 Statistics, Data, and Statistical Thinking

The most common type of observational study is a survey , where the researcher

samples a group of people, asks one or more questions, and records the responses Probably the most familiar type of survey is the political poll, conducted by any one of

a number of organizations (e.g., Harris, Gallup, Roper, and CNN) and designed to dict the outcome of a political election Another familiar survey is the Nielsen survey, which provides the major networks with information on the most-watched programs on television Surveys can be conducted through the mail, with telephone interviews, or with in-person interviews Although in-person surveys are more expensive than mail or telephone surveys, they may be necessary when complex information is to be collected

A designed experiment is a data collection method where the researcher exerts full

control over the characteristics of the experimental units sampled These

experi-ments typically involve a group of experimental units that are assigned the treatment and an untreated (or control ) group

An observational study is a data collection method where the experimental units

sampled are observed in their natural setting No attempt is made to control the

characteristics of the experimental units sampled (Examples include opinion polls and surveys )

A representative sample exhibits characteristics typical of those possessed by the

target population

For example, consider a political poll conducted during a presidential election year Assume that the pollster wants to estimate the percentage of all 145 million registered voters in the United States who favor the incumbent president The pollster would be unwise to base the estimate on survey data collected for a sample of voters from the

incumbent’s own state Such an estimate would almost certainly be biased high;

conse-quently, it would not be very reliable

The most common way to satisfy the representative sample requirement is to

select a random sample A random sample ensures that every subset of fixed size in the

population has the same chance of being included in the sample If the pollster samples 1,500 of the 145 million voters in the population so that every subset of 1,500 voters has

an equal chance of being selected, she has devised a random sample The procedure for selecting a random sample is discussed in Chapter 3 Here, we look at two examples involving actual sampling studies

A random sample of n experimental units is a sample selected from the population

in such a way that every different sample of size n has an equal chance of selection

(See Section 3.7 for how to generate a random sample.) *

Regardless of which data collection method is employed, it is likely that the data will be a sample from some population And if we wish to apply inferential statistics, we

must obtain a representative sample

* In statistical sampling theory, this sample is better known as a simple random sample

Problem What percentage of Web users are addicted to the Internet?

To find out, a psychologist designed a series of 10 questions based on a widely used set of criteria for gambling addiction and distributed them

through the Web site ABCNews.com (A sample question: “Do you

use the Internet to escape problems?”) A total of 17,251 Web users responded to the questionnaire If participants answered

“yes” to at least half of the questions, they were viewed

as addicted The findings, released at an annual meeting

of the American Psychological Association, revealed that

990 respondents, or 5.7%, are addicted to the Internet

a Identify the data collection method

b Identify the target population

c Are the sample data representative of the population?

Solution

a The data collection method is a survey: 17,251 Internet users responded to the

ques-tions posed at the ABCNews.com Web site

b Since the Web site can be accessed by anyone surfing the Internet, presumably the

target population is all Internet users

c Because the 17,251 respondents clearly make up a subset of the target population,

they do form a sample Whether or not the sample is representative is unclear, since

we are given no information on the 17,251 respondents However, a survey like this

one in which the respondents are self-selected (i.e., each Internet user who saw the survey chose whether or not to respond to it) often suffers from nonresponse bias

It is possible that many Internet users who chose not to respond (or who never saw the survey) would have answered the questions differently, leading to a higher (or lower) percentage of affirmative answers

Problem Psychologists at the University of Tennessee carried out a study of the

suscepti-bility of people to hypnosis ( Psychological Assessment , Mar 1995) In a random sample

of 130 undergraduate psychology students at the university, each experienced both ditional hypnosis and computer-assisted hypnosis Approximately half were randomly assigned to undergo the traditional procedure first, followed by the computer-assisted procedure The other half were randomly assigned to experience computer-assisted hypnosis first, then traditional hypnosis Following the hypnosis episodes, all students filled out questionnaires designed to measure a student’s susceptibility to hypnosis The susceptibility scores of the two groups of students were compared

a Identify the data collection method

b Is the sample data representative of the target population?

Solution

a Here, the experimental units are the psychology students Since the researchers

controlled which type of hypnosis—traditional or computer assisted—the students experienced first (through random assignment), a designed experiment was used to collect the data

b The sample of 130 psychology students was randomly selected from all psychology

students at the University of Tennessee If the target population is all University

of Tennessee psychology students , it is likely that the sample is representative However, the researchers warn that the sample data should not be used to make inferences about other, more general, populations