Ebook Business statistics (2nd edition): Part 1

(BQ) Part 1 book Business statistics has contents: Statistics and variation, surveys and sampling, displaying and describing categorical data, displaying and describing quantitative data, correlation and linear regression, randomness and probability, random variables and probability models,...and other contents.

Business Statistics

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Sharpe, Norean Radke.

Business statistics / Norean R Sharpe, Richard D De Veaux, Paul F.

Velleman; with contributions from Dave Bock — 2nd ed.

To my parents, who taught me the importance of education

As a researcher of statistical problems in business and a professor of Statistics at a business school, Norean Radke Sharpe(Ph.D University of Virginia) understands the challenges and specific needs of the business student She is currently teaching at the McDonough School of Business at Georgetown University, where she is also Associate Dean and Director of Undergraduate Programs Prior to joining Georgetown, she taught business statistics and operations research courses to both undergraduate and MBA students for fourteen years at Babson College Before moving into business education, she taught mathematics for several years at Bowdoin College and conducted research at

Yale University Norean is coauthor of the recent text, A Casebook for Business Statistics: Laboratories for Decision Making, and she has authored more than 30 articles—primarily in

the areas of statistics education and women in science Norean currently serves as Associate

Editor for the journal Cases in Business, Industry, and Government Statistics Her research

focuses on business forecasting and statistics education She is also co-founder of DOME Foundation, Inc., a nonprofit foundation that works to increase Diversity and Outreach

in Mathematics and Engineering for the greater Boston area She has been active in increasing the participation of women and underrepresented students in science and mathematics for several years and has two children of her own.

Richard D De Veaux(Ph.D Stanford University) is an internationally known educator, consultant, and lecturer Dick has taught statistics at a business school (Wharton),

an engineering school (Princeton), and a liberal arts college (Williams) While at Princeton, he won a Lifetime Award for Dedication and Excellence in Teaching Since

1994, he has been a professor of statistics at Williams College, although he returned to Princeton for the academic year 2006–2007 as the William R Kenan Jr Visiting Professor

of Distinguished Teaching Dick holds degrees from Princeton University in Civil Engineering and Mathematics and from Stanford University in Dance Education and Statistics, where he studied with Persi Diaconis His research focuses on the analysis of large data sets and data mining in science and industry Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality and is a Fellow of the American

Statistical Association Dick is well known in industry, having consulted for such Fortune

500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank He was named the “Statistician of the Year” for 2008 by the Boston Chapter of the American Statistical Association for his contributions to teaching, research, and consulting In his spare time he is an avid cyclist and swimmer He also is the founder and bass for the doo-wop group, the Diminished Faculty, and is a frequent soloist with various local choirs and orchestras Dick is the father of four children.

Paul F Velleman(Ph.D Princeton University) has an international reputation for innovative statistics education He designed the Data Desk ® software package and is also the author and designer of the award-winning ActivStats ® multimedia software, for which he received the EDUCOM Medal for innovative uses of computers in teaching statistics and the ICTCM Award for Innovation in Using Technology in College Mathematics He is the founder and CEO of Data Description, Inc (www.datadesk.com), which supports both of

these programs He also developed the Internet site, Data and Story Library (DASL; www.

dasl.datadesk.com), which provides data sets for teaching Statistics Paul coauthored (with

David Hoaglin) the book ABCs of Exploratory Data Analysis Paul has taught Statistics at

Cornell University on the faculty of the School of Industrial and Labor Relations since 1975 His research often focuses on statistical graphics and data analysis methods Paul is a Fellow

of the American Statistical Association and of the American Association for the Advancement

of Science He is also baritone of the barbershop quartet Rowrbazzle! Paul’s experience as a professor, entrepreneur, and business leader brings a unique perspective to the book.

Richard De Veaux and Paul Velleman have authored successful books in the introductory

college and AP High School market with David Bock, including Intro Stats, Third Edition (Pearson, 2009), Stats: Modeling the World, Third Edition (Pearson, 2010), and Stats: Data and Models, Third Edition (Pearson, 2012).

Meet the Authors

iv

v

Part I Exploring and Collecting Data

1.1So, What Is Statistics?•1.2How Will This Book Help?

2.1What Are Data?•2.2Variable Types•2.3Data Sources: Where, How, and When

3.1Three Ideas of Sampling•3.2Populations and Parameters•3.3Common Sampling Designs•3.4The Valid Survey•3.5How to Sample Badly

Brief Cases: Market Survey Research and The GfK Roper Reports Worldwide Survey 44

4.1Summarizing a Categorical Variable•4.2Displaying a Categorical Variable•4.3Exploring Two Categorical Variables: Contingency Tables

Technology Help: Displaying Categorical Data on the Computer 71

5.1Displaying Quantitative Variables•5.2Shape•5.3Center•5.4Spread

of the Distribution•5.5Shape, Center, and Spread—A Summary•5.6Five-Number Summary and Boxplots•5.7Comparing Groups•5.8Identifying Outliers•5.9Standardizing•5.10Time Series Plots•5.11Transforming Skewed Data

Technology Help: Displaying and Summarizing Quantitative Variables 119 Brief Cases: Hotel Occupancy Rates and Value and Growth Stock Returns 121

Chapter 6 Correlation and Linear Regression (Lowe’s) 137

6.1Looking at Scatterplots•6.2Assigning Roles to Variables in Scatterplots•6.3Understanding Correlation•6.4Lurking Variables andCausation•6.5The Linear Model•6.6Correlation and the Line•6.7Regression to the Mean•6.8Checking the Model•6.9Variation in the

Model and R2•6.10Reality Check: Is the Regression Reasonable?•6.11Nonlinear Relationships

Technology Help: Correlation and Regression 171 Brief Cases: Fuel Efficiency and The U.S Economy and the Home Depot Stock Prices 173

Part II Modeling with Probability

7.1Random Phenomena and Probability•7.2The Nonexistent Law

of Averages•7.3Different Types of Probability•7.4Probability Rules•7.5Joint Probability and Contingency Tables•7.6Conditional

Probability•7.7Constructing Contingency Tables

8.1Expected Value of a Random Variable•8.2Standard Deviation of

a Random Variable•8.3Properties of Expected Values and Variances•8.4Discrete Probability Distributions

9.1The Standard Deviation as a Ruler•9.2The Normal Distribution •9.3Normal Probability Plots•9.4The Distribution of Sums of Normals•9.5The Normal Approximation for the Binomial•9.6Other ContinuousRandom Variables

Technology Help: Making Normal Probability Plots 270

10.1The Distribution of Sample Proportions•10.2Sampling Distribution for Proportions•10.3The Central Limit Theorem•10.4The SamplingDistribution of the Mean•10.5How Sampling Distribution Models Work

Brief Cases: Real Estate Simulation: Part 1: Proportions and Part 2: Means 294

Part III Inference for Decision Making

11.1A Confidence Interval•11.2Margin of Error: Certainty

vs Precision•11.3Assumptions and Conditions•11.4Choosing the Sample Size•*11.5 A Confidence Interval for Small Samples

Technology Help: Confidence Intervals for Proportions 321 Brief Cases: Investment and Forecasting Demand 322

12.1The Sampling Distribution for the Mean•12.2A Confidence Interval for Means•12.3Assumptions and Conditions•12.4Cautions About Interpreting Confidence Intervals•12.5Sample Size•12.6Degrees of

Freedom—Why n 1?

Brief Cases: Real Estate and Donor Profiles 348, 349

13.1Hypotheses•13.2A Trial as a Hypothesis Test•13.3P-Values•13.4The Reasoning of Hypothesis Testing•13.5Alternative Hypotheses•13.6Testing Hypothesis about Means—the

One-Sample t-Test•13.7Alpha Levels and Significance•13.8Critical Values•13.9Confidence Intervals and Hypothesis Tests•13.10Two Types of Errors•13.11Power

Brief Cases: Metal Production and Loyalty Program 388

14.1Comparing Two Means•14.2The Two-Sample t-Test•14.3Assumptions and Conditions•14.4A Confidence Interval for the Difference Between Two Means•14.5The Pooled t-Test •

*14.6 Tukey’s Quick Test•14.7Paired Data•14.8The Paired t-Test

Brief Cases: Real Estate and Consumer Spending Patterns (Data Analysis) 431

15.1Goodness-of-Fit Tests•15.2Interpreting Chi-Square Values•15.3Examining the Residuals•15.4The Chi-Square Test of Homogeneity•15.5Comparing Two Proportions•15.6Chi-Square Test of Independence

Brief Cases: Health Insurance and Loyalty Program 475, 476

-Contents vii

Part IV Models for Decision Making

16.1The Population and the Sample•16.2Assumptions and Conditions•16.3The Standard Error of the Slope•16.4A Test for the Regression Slope•16.5A Hypothesis Test for Correlation•16.6Standard Errors forPredicted Values•16.7Using Confidence and Prediction Intervals

Brief Cases: Frozen Pizza and Global Warming? 516

17.1Examining Residuals for Groups•17.2Extrapolation and Prediction•17.3Unusual and Extraordinary Observations•17.4Working with Summary Values•17.5Autocorrelation•17.6Transforming (Re-expressing) Data•17.7The Ladder of Powers

Brief Cases: Gross Domestic Product and Energy Sources 560

18.1The Multiple Regression Model•18.2Interpreting Multiple Regression Coefficients•18.3Assumptions and Conditions for the MultipleRegression Model•18.4Testing the Multiple Regression Model•

18.5Adjusted R2, and the F-statistic•*18.6 The Logistic Regression Model

19.1Indicator (or Dummy) Variables•19.2Adjusting for Different Slopes—Interaction Terms•19.3Multiple Regression Diagnostics•19.4BuildingRegression Models•19.5Collinearity•19.6Quadratic Terms

Technology Help: Building Multiple Regression Models 651 Brief Case: Paralyzed Veterans of America 652

20.1What Is a Time Series?•20.2Components of a Time Series•20.3Smoothing Methods•20.4Summarizing Forecast Error•20.5Autoregressive Models•20.6Multiple Regression–based Models•20.7Choosing a Time Series Forecasting Method•20.8Interpreting Time Series Models: The Whole Foods Data Revisited

Brief Cases: Intel Corporation and Tiffany & Co 701

Case Study: Health Care Costs

Part V Selected Topics in Decision Making

21.1Observational Studies•21.2Randomized, Comparative Experiments•21.3The Four Principles of Experimental Design•21.4Experimental Designs•21.5Issues in Experimental Design•21.6Analyzing a Design in One Factor—The One-Way Analysis of Variance•21.7Assumptions and Conditions for ANOVA•*21.8 MultipleComparisons•21.9ANOVA on Observational Data•21.10Analysis ofMultifactor Designs

22.1A Short History of Quality Control•22.2Control Charts for IndividualObservations (Run Charts)•22.3Control Charts for Measurements: and R

Charts•22.4Actions for Out of Control Processes•22.5Control Charts for

Attributes: p Charts and c Charts•22.6Philosophies of Quality Control

Technology Help: Quality Control Charts on the Computer 799

23.1Ranks•23.2The Wilcoxon Rank-Sum/Mann-Whitney Statistic•23.3Kruskal-Wallace Test•23.4Paired Data: The Wilcoxon

Signed-Rank Test•*23.5 Friedman Test for a Randomized Block Design•23.6Kendall’s Tau: Measuring Monotonicity•23.7Spearman’s Rho•23.8When Should You Use Nonparametric Methods?

24.1Actions, States of Nature, and Outcomes•24.2Payoff Tables and Decision Trees•24.3Minimizing Loss and Maximizing Gain•24.4TheExpected Value of an Action•24.5Expected Value with Perfect

Information•24.6Decisions Made with Sample Information•24.7Estimating Variation•24.8Sensitivity•24.9Simulation•24.10Probability Trees•*24.11 Reversing the Conditioning:

Bayes’s Rule•24.12More Complex Decisions

Brief Cases: Texaco-Pennzoil and Insurance Services, Revisited 857, 858

25.1Direct Marketing•25.2The Data•25.3The Goals of Data Mining•25.4Data Mining Myths•25.5Successful Data Mining•25.6Data Mining Problems•25.7Data Mining Algorithms•25.8The Data Mining Process•25.9Summary

Appendixes

We set out to write a book for business students that answers the simple question:

“How can I make better decisions?” As entrepreneurs and consultants, we know thatknowledge of Statistics is essential to survive and thrive in today’s competitiveenvironment As educators, we’ve seen a disconnect between the way Statistics istaught to business students and the way it is used in making business decisions In

Business Statistics, we try to narrow the gap between theory and practice by presenting

statistical methods so they are both relevant and interesting

The data that inform a business decision have a story to tell, and the role ofStatistics is to help us hear that story clearly and communicate it to others Like

other textbooks, Business Statistics teaches methods and concepts But, unlike other textbooks, Business Statistics also teaches the “why” and insists that results be

reported in the context of business decisions Students will come away knowinghow to think statistically to make better business decisions and how to effectivelycommunicate the analysis that led to the decision to others Our approach requiresup-to-date, real-world examples and current data So, we constantly strive to placeour teaching in the context of current business issues and to illustrate concepts withcurrent examples

What’s New in This Edition?

Our overarching goal in the second edition of Business Statistics has been to organize

the presentation of topics clearly and provide a wealth of examples and exercises sothat the story we tell is always tied to the ways Statistics informs sound businesspractice

Improved Organization The Second Edition has been re-designed from the

ground up We have retained our “data first” presentation of topics because we findthat it provides students with both motivation and a foundation in real businessdecisions on which to build an understanding But we have reorganized the order oftopics within chapters, and the order of chapters themselves to tell the story ofStatistics in Business more clearly

• Chapters 1–6 are now devoted entirely to collecting, displaying, summarizing,and understanding data We find that this gives students a solid foundation tolaunch their understanding of statistical inference

• Material on randomness and probability is now grouped together in ters 7–10 Material on continuous probability models has been gathered into

Chap-a single chChap-apter—ChChap-apter 9, which Chap-also introduces the NormChap-al model

• Core material on inference follows in Chapters 11–15 We introduce ence by discussing proportions because most students are better acquaintedwith proportions reported in surveys and news stories However, this edition

infer-xi

ties in the discussion of means immediately so students can appreciate that thereasoning of inference is the same in a variety of contexts.

• Chapters 16–20 cover regression-based models for decision making

• Chapters 21–25 discuss special topics that can be selected according to theneeds of the course and the preferences of the instructor

• Chapters 22 (Quality Control) and 23 (Nonparametric Methods) are new inthis edition

Section Examples Almost every section of every chapter now has a focused

example to illustrate and apply the concepts and methods of that section

Section Exercises Each chapter’s exercises now begin with single-concept

exercises that target section topics This makes it easier to check your understanding

of each topic as you learn it

Recent Data and New Examples We teach with real data whenever possible.

To keep examples and exercises fresh, we’ve updated data throughout the book Newexamples reflect stories in the news and recent economic and business events

Redesigned Chapter Summaries Our What Have We Learned chapter

summaries have been redesigned to specify learning objectives and place key conceptsand skills within those objectives This makes them even more effective as help forstudents preparing for exams

Statistical Case Studies Each chapter still ends with one or two Brief Cases.

Now, in addition, each of the major parts of the book includes a longer case studyusing larger datasets (found on the CD) and open questions to answer using the dataand a computer

Streamlined Technology Help with additional Excel coverage Technology

Help sections are now in easy-to-follow bulleted lists Excel screenshots and coverage

of Excel 2010 appear throughout the book where appropriate

What’s Old in This Edition: Statistical Thinking

For all of our improvements, examples, and updates in this edition of Business Statistics

we haven’t lost sight of our original mission—writing a modern business statistics text

that addresses the importance of statistical thinking in making business decisions and

that acknowledges how Statistics is actually used in business

Today Statistics is practiced with technology This insight informs everythingfrom our choice of forms for equations (favoring intuitive forms over calculationforms) to our extensive use of real data But most important, understanding the value

of technology allows us to focus on teaching statistical thinking rather than calculation.The questions that motivate each of our hundreds of examples are not “how do youfind the answer?” but “how do you think about the answer, and how does it help youmake a better decision?”

Our focus on statistical thinking ties the chapters of the book together Anintroductory Business Statistics course covers an overwhelming number of newterms, concepts, and methods We have organized these to enhance learning But it

is vital that students see their central core: how we can understand more about theworld and make better decisions by understanding what the data tell us From thisperspective, it is easy to see that the patterns we look for in graphs are the same asthose we think about when we prepare to make inferences And it is easy to see thatthe many ways to draw inferences from data are several applications of the same coreconcepts And it follows naturally that when we extend these basic ideas into morecomplex (and even more realistic) situations, the same basic reasoning is still atthe core of our analyses

Preface xiii

Our Goal: Read This Book!

The best textbook in the world is of little value if it isn’t read Here are some of the

ways we made Business Statistics more approachable:

• Readability We strive for a conversational, approachable style, and we

intro-duce anecdotes to maintain interest While using the First Edition, instructorsreported (to their amazement) that their students read ahead of their assign-

ments voluntarily Students write to tell us (to their amazement) that they

actually enjoy the book

• Focus on assumptions and conditions More than any other textbook, Business

Statistics emphasizes the need to verify assumptions when using statistical

procedures We reiterate this focus throughout the examples and exercises

We make every effort to provide templates that reinforce the practice ofchecking these assumptions and conditions, rather than rushing through thecomputations of a real-life problem

• Emphasis on graphing and exploring data Our consistent emphasis on the

importance of displaying data is evident from the first chapters on standing data to the sophisticated model-building chapters at the end Exam-ples often illustrate the value of examining data graphically, and the Exercisesreinforce this Good graphics reveal structures, patterns, and occasionalanomalies that could otherwise go unnoticed These patterns often raise newquestions and inform both the path of a resulting statistical analysis and thebusiness decisions The graphics found throughout the book also demonstratethat the simple structures that underlie even the most sophisticated statisticalinferences are the same ones we look for in the simplest examples That helps

under-to tie the concepts of the book under-together under-to tell a coherent sunder-tory

• Consistency We work hard to avoid the “do what we say, not what we do”

trap Having taught the importance of plotting data and checking assumptionsand conditions, we are careful to model that behavior throughout the book.(Check the Exercises in the chapters on multiple regression or time series andyou’ll find us still requiring and demonstrating the plots and checks that wereintroduced in the early chapters.) This consistency helps reinforce these fun-damental principles and provides a familiar foundation for the more sophisti-cated topics

• The need to read In this book, important concepts, definitions, and sample

solutions are not always set aside in boxes The book needs to be read, sowe’ve tried to make the reading experience enjoyable The common approach

of skimming for definitions or starting with the exercises and looking up ples just won’t work here (It never did work as a way to learn Statistics; we’vejust made it impractical in our text.)

exam-Coverage

The topics covered in a Business Statistics course are generally mandated by our

students’ needs in their studies and in their future professions But the order of these topics and the relative emphasis given to each is not well established Business Statistics

presents some topics sooner or later than other texts Although many chapters can betaught in a different order, we urge you to consider the order we have chosen.We’ve been guided in the order of topics by the fundamental goal of designing acoherent course in which concepts and methods fit together to provide a newunderstanding of how reasoning with data can uncover new and important truths.Each new topic should fit into the growing structure of understanding that studentsdevelop throughout the course For example, we teach inference concepts with

proportions first and then with means Most people have a wider experience withproportions, seeing them in polls and advertising And by starting with proportions,

we can teach inference with the Normal model and then introduce inference for means

with the Student’s t distribution.

We introduce the concepts of association, correlation, and regression early in Business Statistics Our experience in the classroom shows that introducing these fundamental ideas

early makes Statistics useful and relevant even at the beginning of the course Later in thesemester, when we discuss inference, it is natural and relatively easy to build on thefundamental concepts learned earlier by exploring data with these methods

We’ve been guided in our choice of what to emphasize by the GAISE (Guidelinesfor Assessment and Instruction in Statistics Education) Report, which emerged fromextensive studies of how students best learn Statistics (http://www.amstat.org/education/gaise/ ) Those recommendations, now officially adopted and recommended

by the American Statistical Association, urge (among other detailed suggestions) thatStatistics education should:

1 Emphasize statistical literacy and develop statistical thinking;

2 Use real data;

3 Stress conceptual understanding rather than mere knowledge of procedures;

4 Foster active learning;

5 Use technology for developing conceptual understanding and analyzingdata; and

6 Make assessment a part of the learning process

In this sense, this book is thoroughly modern

Syllabus Flexibility

But to be effective, a course must fit comfortably with the instructor’s preferences.The early chapters—Chapters 1–15—present core material that will be part of anyintroductory course Chapters 16–21—multiple regression, time series, model building,and Analysis of Variance—may be included in an introductory course, but our orga-nization provides flexibility in the order and choice of specific topics Chapters 22–25may be viewed as “special topics” and selected and sequenced to suit the instructor orthe course requirements

Here are some specific notes:

• Chapter 6, Correlation and Linear Regression, may be postponed until justbefore covering regression inference in Chapters 16 and 17

• Chapter 19, Building Multiple Regression Models, must follow the tory material on multiple regression in Chapter 18

introduc-• Chapter 20, Time Series Analysis, requires material on multiple regressionfrom Chapter 18

• Chapter 21, Design and Analysis of Experiments and Observational ies, may be taught before the material on regression—at any point afterChapter 14

Stud-The following topics can be introduced in any order (or omitted):

• Chapter 15, Inference for Counts: Chi-Square Tests

• Chapter 22, Quality Control

• Chapter 23, Nonparametric Methods

• Chapter 24, Decision Making and Risk

• Chapter 25, Introduction to Data Mining

A textbook isn’t just words on a page A textbook is many features that come together

to form a big picture The features in Business Statistics provide a real-world context for

concepts, help students apply these concepts, promote problem-solving, and integratetechnology—all of which help students understand and see the big picture of BusinessStatistics

Motivating Vignettes.Each chapter opens with a motivating vignette, often taken fromthe authors’ consulting experiences These descriptions of companies—such asAmazon.com, Zillow.com, Keen Inc., and Whole Foods Market—enhance andillustrate the story of each chapter and show how and why statistical thinking is sovital to modern business decision-making We analyze data from or about thecompanies in the motivating vignettes throughout the chapter

For Examples.Almost every section of every chapter includes a focused example thatillustrates and applies the concepts or methods of that section The best way tounderstand and remember a new theoretical concept or method is to see it applied

in a real-world business context That’s what these examples do throughout the book

Step-by-Step Guided Examples.The answer to a statistical question is almost neverjust a number Statistics is about understanding the world and making betterdecisions with data To that end, some examples in each chapter are presented asGuided Examples A thorough solution is modeled in the right column whilecommentary appears in the left column The overall analysis follows our innovative

Plan, Do, Report template That template begins each analysis with a clear question

about a business decision and an examination of the data available (Plan) It then moves to calculating the selected statistics (Do) Finally, it concludes with a Report

that specifically addresses the question To emphasize that our goal is to address

the motivating question, we present the Report step as a business memo that

summarizes the results in the context of the example and states a recommendation

if the data are able to support one To preserve the realism of the example, whenever

it is appropriate, we include limitations of the analysis or models in the concludingmemo, as one should in making such a report

Brief Cases.Each chapter includes one or two Brief Cases that use real data and askstudents to investigate a question or make a decision Students define the objective,plan the process, complete the analysis, and report a conclusion Data for the BriefCases are available on the CD and website, formatted for various technologies

Case Studies.Each part of the book ends with a Case Study Students are givenrealistically large data sets (on the CD) and challenged to respond to open-endedbusiness questions using the data Students have the opportunity to bring togethermethods they have learned in the chapters of that part (and indeed, throughout thebook) to address the issues raised Students will have to use a computer to work withthe large data sets that accompany these Case Studies

What Can Go Wrong?Each chapter contains an innovative section called “What Can

Go Wrong?” which highlights the most common statistical errors and themisconceptions about Statistics The most common mistakes for the new user ofStatistics involve misusing a method—not miscalculating a statistic Most of themistakes we discuss have been experienced by the authors in a business context or aclassroom situation One of our goals is to arm students with the tools to detectstatistical errors and to offer practice in debunking misuses of Statistics, whetherintentional or not In this spirit, some of our exercises probe the understanding ofsuch errors

Preface xv

PLAN

DO

REPORT

By Hand.Even though we encourage the use of technology to calculate statisticalquantities, we recognize the pedagogical benefits of occasionally doing a calculation

by hand The By Hand boxes break apart the calculation of some of the simplerformulas and help the student through the calculation of a worked example

Reality Check.We regularly offer reminders that Statistics is about understandingthe world and making decisions with data Results that make no sense are probablywrong, no matter how carefully we think we did the calculations Mistakes are ofteneasy to spot with a little thought, so we ask students to stop for a reality check beforeinterpreting results

Notation Alert. Throughout this book, we emphasize the importance of clearcommunication Proper notation is part of the vocabulary of Statistics, but it can be

daunting We all know that in Algebra n can stand for any variable, so it may be surprising to learn that in Statistics n is always and only the sample size Statisticians dedicate many letters and symbols for specific meanings (b, e, n, p, q, r, s, t, and z,

along with many Greek letters, all carry special connotations) To learn Statistics, it

is vital to be clear about the letters and symbols statisticians use

Just Checking.It is easy to start nodding in agreement without really understanding,

so we ask questions at points throughout the chapter These questions are a quickcheck; most involve very little calculation The answers are at the end of the exercisesets in each chapter to make them easy to check The questions can also be used tomotivate class discussion

Math Boxes.In many chapters, we present the mathematical underpinnings of thestatistical methods and concepts Different students learn in different ways, and anyreader may understand the material best by more than one path We set proofs,derivations, and justifications apart from the narrative, so the underlying mathematics

is there for those who want greater depth, but the text itself presents the logicaldevelopment of the topic at hand without distractions

What Have We Learned? These chapter-ending summaries highlight the majorlearning objectives of the chapter In that context, we review the concepts, define theterms introduced in the chapter, and list the skills that form the core message ofthe chapter These make excellent study guides: the student who understands theconcepts in the summary, knows the terms, and has the skills is probably ready forthe exam

Ethics in Action.Statistics is not just plugging numbers into formulas; most statisticalanalyses require a fair amount of judgment The best guidance for these judgments

is that we make an honest and ethical attempt to learn the truth Anything less than

that can lead to poor and even harmful decisions Our Ethics in Action vignettes in

each chapter illustrate some of the judgments needed in statistical analyses, identifypossible errors, link the issues to the American Statistical Association’s EthicalGuidelines, and then propose ethically and statistically sound alternative approaches

Section Exercises. The Exercises for each chapter begin with straightforwardexercises targeted at the topics in each chapter section This is the place to checkunderstanding of specific topics Because they are labeled by section, turning back

to the right part of the chapter to clarify a concept or review a method is easy

Chapter Exercises.These exercises are designed to be more realistic than SectionExercises and to lead to conclusions about the real world They may combineconcepts and methods from different sections We’ve worked hard to make sure theycontain relevant, modern, and real-world questions Many come from news stories;some come from recent research articles Whenever possible, the data are on the

CD and website (always in a variety of formats) so they can be explored further Theexercises marked with a T indicate that the data are provided on the CD (and on

Notation Alert!

the book’s companion website, www.pearsonhighered.com/sharpe) Throughout,

we pair the exercises so that each odd-numbered exercise (with answer in the back

of the book) is followed by an even-numbered exercise on the same Statistics topic.Exercises are roughly ordered within each chapter by both topic and by level ofdifficulty

Data and Sources.Most of the data used in examples and exercises are from world sources Whenever possible, we present the original data as we collected it.Sometimes, due to concerns of confidentiality or privacy, we had to change the values

real-of the data or the names real-of the variables slightly, always being careful to keep thecontext as realistic and true to life as possible Whenever we can, we includereferences to Internet data sources As Internet users know well, URLs can break aswebsites evolve To minimize the impact of such changes, we point as high in theaddress tree as is practical, so it may be necessary to search down into a site for thedata Moreover, the data online may change as more recent values become available.The data we use are usually on the CD and on the companion website, www.pearsonhighered.com/sharpe

Videos with Optional Captioning.Videos, featuring the Business Statistics authors,

review the high points of each chapter The presentations feature the same friendly style and emphasis on critical thinking as the textbook In addition, 10

student-Business Insight Videos (concept videos) feature Deckers, Southwest Airlines,

Starwood, and other companies and focus on statistical concepts as they pertain tothe real world Videos are available with captioning They can be viewed from withinthe online MyStatLab course

Technology Help.In business, Statistics is practiced with computers using a variety

of statistics packages In Business-school Statistics classes, however, Excel is thesoftware most often used Throughout the book, we show examples of Excel outputand offer occasional tips At the end of each chapter, we summarize what studentscan find in the most common software, often with annotated output We then offerspecific guidance for Excel 2007 and 2010, Minitab, SPSS, and JMP, formatted ineasy-to-read bulleted lists (Technology Help for Excel 2003 and Data Desk are onthe accompanying CD.) This advice is not intended to replace the documentationfor any of the software, but rather to point the way and provide startup assistance

An XLStat Appendix in the back of the book features chapter-by-chapter guidance

for using this new Excel add-in The XLStat icon in Technology Help sectionsdirects readers to this XLStat-specific guidance in Appendix B

Preface xvii

Technology Help

Student Supplements

Business Statistics, for-sale student edition (ISBN-13:

978-0-321-71609-5; ISBN-10: 0-321-71609-4)

Student’s Solutions Manual, by Rose Sebastianelli,

University of Scranton, and Linda Dawson, University of

Washington, provides detailed, worked-out solutions to

odd-numbered exercises (ISBN-13: 978-0-321-68940-5;

ISBN-10: 0-321-68940-2)

Excel Manual, by Elaine Newman, Sonoma State University

(ISBN-13: 978-0-321-71615-6; ISBN-10: 0-321-71615-9)

Minitab Manual, by Linda Dawson, University of

Washington, and Robert H Carver, Stonehill College

(ISBN-13: 978-0-321-71610-1; ISBN-10: 0-321-71610-8)

SPSS Manual (download only), by Rita Akin, Santa

Clara University; ISBN-13: 978-0-321-71618-7; ISBN-10:

0-321-71618-3)

Ten Business Insight Videos (concept videos) feature

Deckers, Southwest Airlines, Starwood, and other companies

and focus on statistical concepts as they pertain to the

real world Available with captioning, these 4- to 7-minute

videos can be viewed from within the online MyStatLab

course or at www.pearsonhighered.com/irc (ISBN-13:

978-0-321-73874-5; ISBN-10: 0-321-73874-8)

Video Lectures were scripted and presented by the authors

themselves, reviewing the important points in each chapter

They can be downloaded from MyStatLab

Study Cards for Business Statistics Software Technology

Study Cards for Business Statistics are a convenient resource

for students, with instructions and screenshots for using the

most popular technologies The following Study Cards

are available in print (8-page fold-out cards) and within

MyStatLab: Excel 2010 with XLStat (0-321-74775-5),

Minitab (0-321-64421-2), JMP (0-321-64423-9), SPSS

(0-321-64422-0), R (0-321-64469-7), and StatCrunch

(0-321-74472-1) A Study Card for the native version of

Excel is also available within MyStatLab

Instructor Supplements

Instructor’s Edition contains answers to all exercises.

(ISBN-13: 978-0-321-71612-5; ISBN-10: 0-321-71612-4)

Online Test Bank (download only), by Rose Sebastianelli,

University of Scranton, includes chapter quizzes and

part level tests The Test Bank is available at www.

pearsonhighered.com/irc (ISBN-13: 978-0-321-68936-8;

ISBN-10: 0-321-68936-4)

Instructor’s Solutions Manual, by Rose Sebastianelli,

University of Scranton, and Linda Dawson, University ofWashington, contains detailed solutions to all of the exercises.(ISBN-13: 978-0-321-68935-1; ISBN-10: 0-321-68935-6)

Instructor’s Resource Guide contains chapter-by-chapter

comments on the major concepts, tips on presenting topics(and what to avoid), teaching examples, suggested assignments,basic exercises, and web links and lists of other resources.Available within MyStatLab or at www.pearsonhighered com/irc

Lesson Podcasts for Business Statistics These audio

podcasts from the authors focus on the key points of eachchapter, helping both new and experienced instructorsprepare for class; available in MyStatLab or at www pearsonhighered.com/irc (ISBN-13: 978-0-321-74688-7;ISBN-10: 0-321-74688-0)

Business Insight Video Guide to accompany Business

Statistics Written to accompany the Business Insight Videos,this guide includes a summary of the video, video-specificquestions and answers that can be used for assessment orclassroom discussion, a correlation to relevant chapters in

Business Statistics, concept-centered teaching points, and

useful web links The Video Guide is available for downloadfrom MyStatLab or at www.pearsonhighered.com/irc.PowerPoint®Lecture Slides

PowerPoint Lecture Slides provide an outline to use in alecture setting, presenting definitions, key concepts, andfigures from the text These slides are available withinMyStatLab or at www.pearsonhighered.com/irc

Active Learning QuestionsPrepared in PowerPoint®, these questions are intended foruse with classroom response systems Several multiple-choicequestions are available for each chapter of the book, allowinginstructors to quickly assess mastery of material in class TheActive Learning Questions are available to download fromwithin MyStatLab and from the Pearson Education onlinecatalog

Technology Resources

A companion CD is bound in new copies of Business Statistics.

The CD holds the following supporting materials, including:

• Data for exercises marked in the text are available

on the CD and website formatted for Excel, JMP,Minitab 14 and 15, SPSS, and as text files suitable forthese and virtually any other statistics software

T

Supplements

• XLStat for Pearson The CD includes a launch page

and instructions for downloading and installing this

Excel add-in Developed in 1993, XLStat is used by

leading businesses and universities around the world

It is compatible with all Excel versions from version 97

to version 2010 (except 2008 for Mac), and is compatible

with the Windows 9x through Windows 7 systems, as

well as with the PowerPC and Intel based Mac systems

For more information, visit www.pearsonhighered.

com/xlstat

ActivStats ®for Business Statistics (Mac and PC).The

award-winning ActivStats multimedia program supports

learning chapter by chapter with the book It complements

the book with videos of real-world stories, worked examples,

animated expositions of each of the major Statistics topics,

and tools for performing simulations, visualizing inference,

and learning to use statistics software ActivStats includes 15

short video clips; 183 animated activities and teaching

applets; 260 data sets; interactive graphs, simulations,

visualization tools, and much more ActivStats for Business

Statistics (Mac and PC) is available in an all-in-one version

for Excel, JMP, Minitab, and SPSS (ISBN-13:

978-0-321-57719-1; ISBN-10: 0-321-57719-1)

MyStatLab™ Online Course (access

code required)

MyStatLab™—part of the MyMathLab®product family—

is a text-specific, easily customizable online course that

integrates interactive multimedia instruction with textbook

content MyStatLab gives you the tools you need to deliver

all or a portion of your course online, whether your students

are in a lab setting or working from home

• Interactive homework exercises, correlated to your

textbook at the objective level, are algorithmically

generated for unlimited practice and mastery Most

exercises are free-response and provide guided

solu-tions, sample problems, and learning aids for extra

help StatCrunch, an online data analysis tool, is

avail-able with online homework and practice exercises

• Personalized homework assignments that you can

design to meet the needs of your class MyStatLab

tailors the assignment for each student based on their

test or quiz scores Each student receives a homework

assignment that contains only the problems they still

need to master

• A Personalized Study Plan, generated when students

complete a test or quiz or homework, indicates which

topics have been mastered and links to tutorial

exer-cises for topics students have not mastered You can

customize the Study Plan so that the topics available

match your course content

• Multimedia learning aids, such as video lectures and

podcasts, animations, and a complete multimedia book, help students independently improve their un-derstanding and performance You can assign thesemultimedia learning aids as homework to help yourstudents grasp the concepts In addition, applets arealso available to display statistical concepts in a graph-ical manner for classroom demonstration or inde-pendent use

text-• StatCrunch.com access is now included with

MyStat-Lab StatCrunch.com is the first web-based dataanalysis tool designed for teaching statistics Users canperform complex analyses, share data sets, and gener-ate compelling reports The vibrant online commu-nity offers more than ten thousand data sets for stu-dents to analyze

• Homework and Test Manager lets you assign

home-work, quizzes, and tests that are automatically graded.Select just the right mix of questions from theMyStatLab exercise bank, instructor-created customexercises, and/or TestGen test items

• Gradebook, designed specifically for mathematics and

statistics, automatically tracks students’ results, letsyou stay on top of student performance, and gives youcontrol over how to calculate final grades Youcan also add offline (paper-and-pencil) grades to thegradebook

• MathXL Exercise Builder allows you to create static

and algorithmic exercises for your online assignments.You can use the library of sample exercises as an easystarting point or use the Exercise Builder to edit any

of the course-related exercises

• Pearson Tutor Center ( www.pearsontutorservices com) access is automatically included with MyStat-Lab The Tutor Center is staffed by qualified statisticsinstructors who provide textbook-specific tutoring forstudents via toll-free phone, fax, email, and interactiveWeb sessions

Students do their assignments in the Flash®-basedMathXL Player which is compatible with almost any browser(Firefox®, Safari™, or Internet Explorer®) on almost anyplatform (Macintosh®or Windows®) MyStatLab is powered

by CourseCompass™, Pearson Education’s online teachingand learning environment, and by MathXL®, our onlinehomework, tutorial, and assessment system MyStatLab isavailable to qualified adopters For more information, visit

www.mystatlab.comor contact your Pearson representative

Preface xix

MyLabsPlus combines effective teaching and learning

materials from MyStatLab™ with convenient management

tools and a dedicated services team It is designed to support

growing math and statistics programs and includes additional

features such as:

• Batch Enrollment: Your school can create the login

name and password for every student and instructor,

so everyone can be ready to start class on the first day

Automation of this process is also possible through

integration with your school’s Student Information

System

• Login from your campus portal: You and your

stu-dents can link directly from your campus portal into

your MyLabsPlus courses A Pearson service team

works with your institution to create a single sign-on

experience for instructors and students

• Diagnostic Placement: Students can take a placement

exam covering reading, writing, and mathematics to

assess their skills You get the results immediately, and

you may customize the exam to meet your

depart-ment’s specific needs

• Advanced Reporting: MyLabsPlus’s advanced

report-ing allows instructors to review and analyze students’

strengths and weaknesses by tracking their

perfor-mance on tests, assignments, and tutorials

Adminis-trators can review grades and assignments across all

courses on your MyLabsPlus campus for a broad

overview of program performance

• 24/7 Support: Students and instructors receive 24/7

support, 365 days a year, by phone, email, or online

chat

MyLabsPlus is available to qualified adopters For more

information, visit our website at www.mylabsplus.comor

contact your Pearson representative

MathXL® for Statistics Online Course

(access code required)

MathXL®for Statistics is an online homework, tutorial, and

assessment system that accompanies Pearson’s textbooks in

statistics MathXL for Statistics is available to qualified

adopters For more information, visit our website at www.

mathxl.com, or contact your Pearson representative

StatCrunch™

StatCrunch™ is web-based statistical software that allows

users to perform complex analyses, share data sets, and

generate compelling reports of their data Users can uploadtheir own data to StatCrunch, or search the library of overtwelve thousand publicly shared data sets, covering almostany topic of interest Interactive graphical outputs helpusers understand statistical concepts, and are available forexport to enrich reports with visual representations of data.Additional features include:

• A full range of numerical and graphical methods thatallow users to analyze and gain insights from any dataset

• Reporting options that help users create a wide variety

of visually appealing representations of their data

• An online survey tool that allows users to quicklybuild and administer surveys via a web form

StatCrunch is available to qualified adopters For moreinformation, visit our website at www.statcrunch.com, orcontact your Pearson representative

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

to build, edit, print, and administer tests using a computerizedbank of questions developed to cover all the objectives of thetext TestGen is algorithmically based, allowing instructors

to create multiple but equivalent versions of the samequestion or test with the click of a button Instructors canalso modify test bank questions or add new questions Thesoftware and testbank are available for download fromPearson Education’s online catalog

Pearson Math & Statistics Adjunct Support Center

The Pearson Math & Statistics Adjunct Support Center (http://www.pearsontutorservices.com/math-adjunct.html)

is staffed by qualified instructors with more than 100 years ofcombined experience at both the community college anduniversity levels Assistance is provided for faculty in thefollowing areas:

• Suggested syllabus consultation

• Tips on using materials packed with your book

• Book-specific content assistance

• Teaching suggestions, including advice on classroomstrategies

Companion Website for Business Statistics, 2nd edition,

includes all of the datasets needed for the book in severalformats, tables and selected formulas, and a quick guide toinference Access this website at www.pearsonhighered com/sharpe

This book would not have been possible without many contributions from DavidBock, our co-author on several other texts Many of the explanations and exercises inthis book benefit from Dave's pedagogical flair and expertise We are honored to havehim as a colleague and friend

Many people have contributed to this book from the first day of its conception to

its publication Business Statistics would have never seen the light of day without the

assistance of the incredible team at Pearson Our Editor in Chief, Deirdre Lynch, wascentral to the support, development, and realization of the book from day one ChereBemelmans, Senior Content Editor, kept us on task as much as humanly possible PeggyMcMahon, Senior Production Project Manager, and Laura Hakala, Senior ProjectManager at PreMedia Global, worked miracles to get the book out the door We areindebted to them Dana Jones, Associate Content Editor; Alex Gay, Senior MarketingManager; Kathleen DeChavez, Marketing Associate; and Dona Kenly, Senior MarketDevelopment Manager, were essential in managing all of the behind-the-scenes workthat needed to be done Aimee Thorne, Media Producer, put together a top-notchmedia package for this book Barbara Atkinson, Senior Designer, and Studio Montageare responsible for the wonderful way the book looks Evelyn Beaton, ManufacturingManager, along with Senior Manufacturing Buyers Carol Melville and Ginny Michaud,worked miracles to get this book and CD in your hands, and Greg Tobin, President,was supportive and good-humored throughout all aspects of the project

Special thanks go out to PreMedia Global, the compositor, for the wonderfulwork they did on this book and in particular to Laura Hakala, the project manager, forher close attention to detail We’d also like to thank our accuracy checkers whosemonumental task was to make sure we said what we thought we were saying: JackieMiller, The Ohio State University; Dirk Tempelaar, Maastricht University; andNicholas Gorgievski, Nichols College

We wish to thank the following individuals who joined us for a weekend to discussbusiness statistics education, emerging trends, technology, and business ethics These

individuals made invaluable contributions to Business Statistics:

Dr Taiwo Amoo, CUNY Brooklyn

Dave Bregenzer, Utah State University

Joan Donohue, University of South Carolina

Soheila Fardanesh, Towson University

Chun Jin, Central Connecticut State University

Brad McDonald, Northern Illinois University

Amy Luginbuhl Phelps, Duquesne University

Michael Polomsky, Cleveland State University

Robert Potter, University of Central Florida

Rose Sebastianelli, University of Scranton

Debra Stiver, University of Nevada, Reno

Minghe Sun, University of Texas—San Antonio

Mary Whiteside, University of Texas—Arlington

We also thank those who provided feedback through focus groups, class tests, and views (reviewers of the second edition are in boldface):

re-Alabama: Nancy Freeman, Shelton State Community College; Rich Kern,

Montgomery County Community College; Robert Kitahara, Troy University; Tammy

Prater, Alabama State University Arizona: Kathyrn Kozak, Coconino Community

College; Robert Meeks, Pima Community College; Philip J Mizzi, Arizona State

University; Eugene Round, Embry-Riddle Aeronautical University; Yvonne Sandoval, Pima Community College; Alex Sugiyama, University of Arizona California: Eugene

Allevato, Woodbury University; Randy Anderson, California State University, Fresno;Paul Baum, California State University, Northridge; Giorgio Canarella, CaliforniaState University, Los Angeles; Natasa Christodoulidou, California State University,Dominguez Hills; Abe Feinberg, California State University, Northridge; Bob Hopfe,

Preface xxi

California State University, Sacramento; John Lawrence, California State University,Fullerton; Elaine McDonald-Newman, Sonoma State University; Khosrow

Moshirvaziri, California State University; Sunil Sapra, California State University, Los

Angeles; Carlton Scott, University of California, Irvine; Yeung-Nan Shieh, San JoseState University; Dr Rafael Solis, California State University, Fresno; T J Tabara,Golden Gate University; Dawit Zerom, California State University, Fullerton

Colorado: Sally Hay, Western State College; Austin Lampros, Colorado State

University; Rutilio Martinez, University of Northern Colorado; Gerald Morris,Metropolitan State College of Denver; Charles Trinkel, DeVry University, Colorado

Connecticut: Judith Mills, Southern Connecticut State University; William Pan,

University of New Haven; Frank Bensics, Central Connecticut State University; LoriFuller, Tunxis Community College; Chun Jin, Central Connecticut State University;

Jason Molitierno, Sacred Heart University Florida: David Afshartous, University of

Miami; Dipankar Basu, Miami University; Ali Choudhry, Florida InternationalUniversity; Nirmal Devi, Embry Riddle Aeronautical University; Dr Chris Johnson,

University of North Florida; Robert Potter, University of Central Florida; Gary Smith, Florida State University; Patrick Thompson, University of Florida; Roman Wong, Barry University Georgia: Hope M Baker, Kennesaw State University; Dr.

Michael Deis, Clayton University; Swarna Dutt, State University of West Georgia;

Kim Gilbert, University of Georgia; John Grout, Berry College; Michael Parzen,

Emory University; Barbara Price, Georgia Southern University; Dimitry Shishkin, Georgia Gwinnett College Idaho: Craig Johnson, Brigham Young University; Teri

Peterson, Idaho State University; Dan Petrak, Des Moines Area Community College

Illinois: Lori Bell, Blackburn College; Jim Choi, DePaul University; David Gordon,

Illinois Valley Community College; John Kriz, Joliet Junior College; ConstantineLoucopoulos, Northeastern Illinois University; Brad McDonald, Northern Illinois

University; Ozgur Orhangazi, Roosevelt University Indiana: H Lane David, Indiana

University South Bend; Ting Liu, Ball State University; Constance McLaren, IndianaState University; Dr Ceyhun Ozgur, Valparaiso University; Hedayeh Samavati, IndianaUniversity, Purdue; Mary Ann Shifflet, University of Southern Indiana; Cliff Stone,

Ball State University; Sandra Strasser, Valparaiso University Iowa: Ann Cannon,

Cornell College; Timothy McDaniel, Buena Vista University; Dan Petrack, DesMoines Area Community College; Mount Vernon, Iowa; Osnat Stramer, University ofIowa; Bulent Uyar, University of Northern Iowa; Blake Whitten, University of Iowa

Kansas: John E Boyer, Jr., Kansas State University Kentucky: Arnold J Stromberg,

University of Kentucky Louisiana: Jim Van Scotter, Louisana State University; Zhiwei Zhu, University of Louisiana at Lafayette Maryland: John F Beyers,

University of Maryland University College; Deborah Collins, Anne ArundelCommunity College; Frederick W Derrick, Loyola College in Maryland; Soheila

Fardanesh, Towson University; Dr Jeffery Michael, Towson University; Dr Timothy

Sullivan, Towson University Massachusetts: Elaine Allen, Babson College; Paul D.

Berger, Bentley College; Scott Callan, Bentley College; Ken Callow, Bay Path College;Robert H Carver, Stonehill College; Richard Cleary, Bentley College; IsmaelDambolena, Babson College; Steve Erikson, Babson College; Elizabeth Haran, SalemState College; David Kopcso, Babson College; Supriya Lahiri, University ofMassachusetts, Lowell; John MacKenzie, Babson College; Dennis Mathaisel, Babson

College; Richard McGowan, Boston College; Abdul Momen, Framingham State

University; Ken Parker, Babson College; John Saber, Babson College; AhmadSaranjam, Bridgewater State College; Daniel G Shimshak, University ofMassachusetts, Boston; Erl Sorensen, Bentley College; Denise Sakai Troxell, BabsonCollege; Janet M Wagner, University of Massachusetts, Boston; Elizabeth Wark,

Worcester State College; Fred Wiseman, Northeastern University Michigan: Kai Chang, Wayne State University Minnesota: Daniel G Brick, University of

Sheng-St Thomas; Dr David J Doorn, University of Minnesota Duluth; Howard Kittleson,Riverland Community College; Craig Miller, Normandale Community College

Mississippi: Dal Didia, Jackson State University; J H Sullivan, Mississippi State

University; Wenbin Tang, The University of Mississippi Missouri: Emily Ross, University of Missouri, St Louis Nevada: Debra K Stiver, University of Nevada, Reno; Grace Thomson, Nevada State College New Hampshire: Parama Chaudhury, Dartmouth College; Doug Morris, University of New Hampshire New Jersey: Kunle

Adamson, DeVry University; Dov Chelst, DeVry University—New Jersey; Leonard

Presby, William Paterson University; Subarna Samanta, The College of New Jersey.

New York: Dr Taiwo Amoo, City University of New York, Brooklyn; Bernard

Dickman, Hofstra University; Mark Marino, Niagara University North Carolina:

Margaret Capen, East Carolina University; Warren Gulko, University of North

Carolina, Wilmington; Geetha Vaidyanathan, University of North Carolina Ohio:

David Booth, Kent State University, Main Campus; Arlene Eisenman, Kent StateUniversity; Michael Herdlick, Tiffin University; Joe Nowakowski, MuskingumCollege; Jayprakash Patankar, The University of Akron; Michael Polomsky, ClevelandState University; Anirudh Ruhil, Ohio University; Bonnie Schroeder, Ohio StateUniversity; Gwen Terwilliger, University of Toledo; Yan Yu, University of Cincinnati

Oklahoma: Anne M Davey, Northeastern State University; Damian Whalen,

St Gregory’s University; David Hudgins, University of Oklahoma—Norman; Dr

William D Warde, Oklahoma State University—Main Campus Oregon: Jodi Fasteen, Portland State University Pennsylvania: Dr Deborah Gougeon, University

of Scranton; Rose Sebastianelli, University of Scranton; Jack Yurkiewicz, PaceUniversity; Rita Akin, Westminster College; H David Chen, Rosemont College;Laurel Chiappetta, University of Pittsburgh; Burt Holland, Temple University; Ronald

K Klimberg, Saint Joseph’s University; Amy Luginbuhl Phelps, Duquesne University;Sherryl May, University of Pittsburg—KGSB; Dr Bruce McCullough, DrexelUniversity; Tracy Miller, Grove City College; Heather O’Neill, Ursinus College; TomShort, Indiana University of Pennsylvania; Keith Wargo, Philadelphia Biblical

University Rhode Island: Paul Boyd, Johnson & Wales University; Nicholas

Gorgievski, Nichols College; Jeffrey Jarrett, University of Rhode Island South Carolina: Karie Barbour, Lander University; Joan Donohue, University of South

Carolina; Woodrow Hughes, Jr., Converse College; Willis Lewis, Lander University;

M Patterson, Midwestern State University; Kathryn A Szabat, LaSalle University

Tennessee: Ferdinand DiFurio, Tennessee Technical University; Farhad Raiszadeh,

University of Tennessee—Chattanooga; Scott J Seipel, Middle Tennessee StateUniversity; Han Wu, Austin Peay State University; Jim Zimmer, Chattanooga State

University Texas: Raphael Azuaje, Sul Ross State University; Mark Eakin, University

of Texas—Arlington; Betsy Greenberg, University of Texas— Austin; Daniel Friesen,Midwestern State University; Erin Hodgess, University of Houston—Downtown;Joyce Keller, St Edward’s University; Gary Kelley, West Texas A&M University;

Monnie McGee, Southern Methodist University; John M Miller, Sam Houston State

University; Carolyn H Monroe, Baylor University; Ranga Ramasesh, Texas ChristianUniversity; Plamen Simeonov, University of Houston— Downtown; Lynne Stokes,

Southern Methodist University; Minghe Sun, University of Texas—San Antonio;

Rajesh Tahiliani University of Texas—El Paso; MaryWhiteside, University of Texas—

Arlington; Stuart Warnock, Tarleton State University Utah: Dave Bregenzer, Utah State University; Camille Fairbourn, Utah State University Virginia: Sidhartha R.

Das, George Mason University; Quinton J Nottingham, Virginia Polytechnic & State

University; Ping Wang, James Madison University Washington: Nancy Birch, Eastern

Washington University; Mike Cicero, Highline Community College; Fred DeKay,Seattle University; Stergios Fotopoulous, Washington State University; Teresa Ling,

Seattle University; Motzev Mihail, Walla Walla University West Virginia: Clifford

Hawley, West Virginia University Wisconsin: Daniel G Brick, University of

St Thomas; Nancy Burnett University of Wisconsin—Oshkosh; Thomas Groleau,

Carthage College; Patricia Ann Mullins, University of Wisconsin, Madison Canada: Jianan Peng, Acadia University; Brian E Smith, McGill University The Netherlands:

Dirk Tempelaar, Maastricht University

Finally, we want to thank our families This has been a long project, and it has requiredmany nights and weekends Our families have sacrificed so that we could write thebook we envisioned

Norean Sharpe

Richard De Veaux

Paul Velleman

Preface xxiii

Index of Applications

Credit Card Charges (E), 122, 327, 329, 352, 530; (GE), 101–102, 376–377, 421–423; (IE), 278, 538–539 Credit Card Companies (BE), 316; (E), 296–297, 327,

329, 352, 398; (GE), 145–146, 376–377, 405–407, 409–411; (IE), 16, 190, 277–278, 316, 399–401, 538–539, 717–719, 863–865; (JC), 375, 379; (P), 20 Credit Card Customers (BE), 316; (E), 242, 327, 329,

352, 389, 484; (GE), 101–102, 376–377, 405–407, 409–411, 421–423; (IE), 277–278, 280, 316, 400–401, 538–539, 718–719; (JC), 375, 379 Credit Card Debt (E), 441; (JC), 375, 379 Credit Card Offers (BE), 316; (E), 327, 329; (GE), 376–377, 405–407, 409–411, 725–726, 745–748;

(IE), 16, 190–191, 278, 316, 400–401, 538–539,

720, 728, 740–742; (P), 20, 879 Credit Scores (IE), 189–190 Credit Unions (EIA), 319 Federal Reserve Bank (E), 208 Federal Reserve Board (BE), 670 Interest Rates (E), 177, 208, 569–570, 709, 715, 834;

(IE), 278, 724; (P), 237–238 Investment Banks (E), 859–860 Liquid Assets (E), 704–705 Maryland Bank National Association (IE), 277–278 Mortgages (E), 23, 177, 834; (GE), 283–284 Subprime Loans (IE), 15, 189

World Bank (E), 133, 181

Business (General)

Attracting New Business (E), 354 Best Places to Work (E), 486, 526–527 Bossnapping (E), 322; (GE), 313–314 Business Planning (IE), 7, 378 Chief Executives (E), 131–132, 214–215, 274, 351, 483–484; (IE), 112–113, 337–338

Company Case Reports and Lawyers (GE), 283–284; (IE), 3

Company Databases (IE), 14, 16, 306 Contract Bids (E), 239–240, 862 Elder Care Business (EIA), 512 Enterprise Resource Planning (E), 440, 486, 831 Entrepreneurial Skills (E), 483

Forbes 500 Companies (E), 134–135, 351–352 Fortune 500 Companies (E), 323, 523; (IE), 337–338, 717

Franchises (BE), 624; (EIA), 168, 512 Industry Sector (E), 485

International Business (E), 45, 74–75, 82, 127, 181–182, 210, 326; (IE), 26; (P), 44 Job Growth (E), 486, 526–527 Organisation for Economic Cooperation and Development (OECD) (E), 127, 574 Outside Consultants (IE), 68 Outsourcing (E), 485 Research and Development (E), 78; (IE), 7–8; (JC), 420 Small Business (E), 75–76, 78, 177, 212, 239, 327,

353, 484, 568, 611, 660, 860–861; (IE), 8, 835–836

Start-Up Companies (E), 22, 328, 396, 858–859; (IE), 137

Trade Secrets (IE), 491 Women-Led Businesses (E), 75, 81, 239, 327, 395

Company Names

Adair Vineyards (E), 123 AIG (GE), 103–104; (IE), 85–87, 90–96 Allied Signal (IE), 794

Allstate Insurance Company (E), 301 Alpine Medical Systems, Inc (EIA), 602 Amazon.com (IE), 7–9, 13–14 American Express (IE), 399 Amtrak (BE), 719 Arby’s (E), 22 Bank of America (IE), 278, 399 Bell Telephone Laboratories (IE), 769, 771 BMW (E), 184

Bolliger & Mabillard Consulting Engineers, Inc (B&M) (IE), 617–618

Buick (E), 180 Burger King (BE), 624; (E), 616; (IE), 624–625 Cadbury Schweppes (E), 74–75

Capital One (IE), 9, 717–718 Chevy (E), 441

Circuit City (E), 296 Cisco Systems (E), 75 Coca-Cola (E), 74, 390 CompUSA (E), 296 Cypress (JC), 145 Data Description (IE), 835–837, 840–842, 844–846 Deliberately Different (EIA), 472

Desert Inn Resort (E), 209 Diners Club (IE), 399 Eastman Kodak (E), 799 eBay (E), 241 Expedia.com (IE), 577 Fair Isaac Corporation (IE), 189–190 Fisher-Price (E), 75

Ford (E), 180, 441; (IE), 37 General Electric (IE), 358, 771, 794, 807 General Motors Corp (BE), 691; (IE), 807 GfK Roper (E), 45, 76–77, 212–213, 326, 482; (GE), 65–66, 462; (IE), 26–27, 30, 58, 64,

458, 459; (P), 44 Google (E), 77–78, 486, 706; (IE), 52–57, 228–230; (P), 72

Guinness & Co (BE), 233; (IE), 331–333 Hershey (E), 74–75

Holes-R-Us (E), 132 The Home Depot (E), 571; (GE), 681–684, 692–695; (IE), 137–138, 685–686, 688–689; (P), 173–174

Home Illusions (EIA), 292 Honda (E), 180 Hostess (IE), 29, 37 IBM (IE), 807 i4cp (IE), 807

Accounting

Administrative and Training Costs (E), 78, 435–436, 483

Annual Reports (E), 75

Audits and Tax Returns (E), 211, 327, 355, 436, 483,

Earnings per Share Ratio (E), 439

Expenses (E), 568; (IE), 10, 15

Financial Close Process (E), 440

IT Costs and Policies (E), 483

Legal Accounting and Reporting Practices (E), 483

Purchase Records (E), 49; (IE), 10, 11

Predicting Sales (E), 183, 184

Product Claims (BE), 401; (E), 274, 442–443, 446–447,

Agricultural Discharge (E), 50; (EIA), 41

Beef and Livestock (E), 351, 614

Drought and Crop Losses (E), 444

Farmers’ Markets (E), 240–241

Fruit Growers (E), 574

Lawn Equipment (E), 860–861

Lobster Fishing Industry (E), 571–573, 575, 613–614,

659–660, 663–664

Lumber (E), 24, 574

Seeds (E), 299, 395

Banking

Annual Percentage Rate (IE), 728; (P), 237–238

ATMs (E), 207; (IE), 399

Bank Tellers (E), 760

Certificates of Deposit (CDs) (P), 237–238

BE = Boxed Example; E = Exercises; EIA = Ethics in Action; GE = Guided Example; IE = In-Text Example and For Example; JC = Just Checking;

P = Project; TH = Technology Help

xxiv

Index of Applications xxv

Consumers

Categorizing Consumers (E), 82, 481–483, 760; (EIA), 319; (IE), 13–14, 28–29, 721; (P), 206 Consumer Confidence Index (CCI) (E), 395;

(IE), 306 Consumer Groups (E), 355, 396, 441 Consumer Loyalty (E), 389; (IE), 2, 532; (JC), 375; (P),

388, 476 Consumer Perceptions About a Product (E), 481–483;

(IE), 618–622 Consumer Price Index (CPI) (E), 271, 612, 614, 656,

703, 708 Consumer Research (E), 214; (IE), 8, 13, 37, 820–821 Consumer Safety (IE), 4

Consumer Spending (E), 183; (GE), 101–102, 145–146, 405–407, 409–411; (IE), 409; (P), 476

Customer Databases (E), 23, 45, 131, 275; (IE), 2, 8–15,

52, 190–191, 864–869; (JC), 64; (P), 22, 388 Customer Satisfaction (E), 242–243, 395, 655; (EIA),

18, 292, 649 Customer Service (E), 50; (EIA), 18, 41; (IE), 8 Hotel Occupancy Rates (P), 121

Restaurant Patrons (JC), 30 Shopping Patterns (E), 122, 123

Demographics

Age (E), 350, 481, 486, 564–565, 569–570; (GE), 467–469; (IE), 466–470; (JC), 342 Average Height (E), 270; (JC), 266 Birth and Death Rates (E), 185, 439, 522 Income (E), 81, 616, 706–707, 834; (IE), 863, 865, 871–872; (JC), 98, 135; (P), 560

Lefties (E), 275 Life Expectancy (E), 574, 616, 657–658; (IE), 148–149, 162

Marital Status (E), 483, 564–565, 569–570 Murder Rate (E), 616

Population (JC), 556; (P), 560 Race/Ethnicity (E), 479, 484 U.S Census Bureau (E), 82, 239, 275, 395, 479; (EIA), 649; (IE), 16, 29, 138, 142, 865; (JC), 30, 98, 339, 342; (P), 560

Using Demographics in Business Analysis (EIA), 877;

(IE), 622, 864–865, 873; (P), 652

Distribution and Operations Management

Construction (E), 762–763 Delivery Services and Times (E), 83, 391, 440, 486;

(EIA), 292 International Distribution (E), 82 Inventory (E), 212, 486; (GE), 221–222 Mail Order (E), 22, 82

Maintenance Costs (E), 395 Mass Merchant Discounters (E), 82 Overhead Costs (E), 75

Packaging (E), 179, 241; (EIA), 292; (GE), 252–254, 257–259

Product Distribution (E), 74–75, 82, 324, 391, 440;

(EIA), 292 Productivity and Efficiency (E), 75, 763 Sales Order Backlog (E), 75 Shipping (BE), 290; (E), 238, 862; (EIA), 292, 472; (GE), 221–222, 257–259

Storage and Retrieval Systems (E), 763–764 Tracking (BE), 290; (E), 83; (IE), 2, 14

Waiting Lines (E), 48, 760; (EIA), 346; (IE), 265–266, 618; (JC), 226

E-Commerce

Advertising and Revenue (E), 175 Internet and Globalization (E), 529–530 Internet Sales (E), 132, 392, 478, 484, 520, 713, 761 Online Businesses (BE), 361, 363, 365, 373; (E), 183,

208, 212, 238, 324, 391, 395, 438–439, 482, 520,

705, 761; (EIA), 319, 472; (IE), 7–9, 12–13, 51–52, 362

Online Sales and Blizzards, 176 Product Showcase Websites (IE), 52–55 Search Engine Research (IE), 53–57; (P), 72 Security of Online Business Transactions (E), 212, 484, 760; (EIA), 472

Special Offers via Websites (EIA), 383; (IE), 13; (P), 388 Tracking Website Hits (E), 239, 243, 274, 388, 758; (IE), 52–57; (P), 72

Web Design, Management, and Sales (E), 211, 391,

758, 861–862; (IE), 362, 371

Economics

Cost of Living (E), 184, 526; (P), 174 Dow Jones Industrial Average (GE), 454, 456; (IE), 357–359, 451–452

Forecasting (E), 208; (IE), 306 Gross Domestic Product (E), 181, 182, 184, 487–488,

565, 573–575, 611, 655–656, 660, 661–662, 833; (EIA), 649; (IE), 691; (P), 560

Growth Rates of Countries (E), 487–488 Human Development Index (E), 565, 575 Inflation Rates (BE), 463–464, 466; (E), 22, 181, 483, 708

Organization for Economic Cooperation and Development (E), 611, 660, 661–662 Personal Consumption Expenditures (EIA), 649 U.S Bureau of Economic Analysis (E), 487–488; (EIA), 649

Views on the Economy (E), 75–76, 326, 389, 393; (IE), 306–309, 311, 318

College Enrollment (JC), 504 College Social Life (JC), 471 College Tuition (E), 124–125, 133, 182–183, 615; (IE), 115

Core Plus Mathematics Project (E), 435 Cornell University (IE), 116

Education and Quality of Life (IE), 162 Education Levels (E), 81, 478, 758–759, 761 Elementary School Attendance Trends (E), 395 Enriched Early Education (IE), 2

Entrance Exams (BE), 250–252; (E), 274, 298–299; (JC), 291

Freshman 15 Weight Gain (E), 830–831 GPA (E), 22, 184, 301; (IE), 153–154 Graduates and Graduation Rates (E), 123, 328, 616; (IE), 464–466

High School Dropout Rates (E), 324, 398

Intel (BE), 671; (IE), 671–672, 675, 677–680; (JC), 145;

Kelly’s BlueBook (E), 214

KomTek Technologies (GE), 786–789

Kraft Foods, Inc (P), 516

Mellon Financial Corporation (E), 704

Metropolitan Life (MetLife) (IE), 217–218

Microsoft (E), 75, 125; (IE), 53, 55–56

M&M/Mars (E), 74–75, 211, 297, 392, 478–479, 761; (GE),

Netflix (E), 241; (IE), 9

Nissan (E), 180; (IE), 255

Sony Corporation (IE), 769–770, 774

Sony France (GE), 313

WebEx Communications (E), 75

Wegmans Food Markets (E), 486

Western Electric (IE), 774–775

Whole Foods Market (BE), 686; (IE), 665–669,

685–692, 696

WinCo Foods (E), 447–448

Wrigley (E), 74–75

Yahoo (E), 706; (IE), 53, 55–56

Zenna’s Café (EIA), 116–117

Zillow.com (IE), 577–578

Internet Piracy (E), 82 Misleading Research (EIA), 18, 235 Sweatshop Labor (IE), 40

Famous People

Armstrong, Lance (IE), 548 Bernoulli, Daniel (IE), 227 Bonferroni, Carlo, 737 Box, George (IE), 248 Castle, Mike (IE), 278 Clooney, George (GE), 313 Cohen, Steven A (IE), 449–450 Deming, W Edward (IE), 770, 771, 793–794

De Moivre, Abraham (IE), 248 Descartes, Rene (IE), 141 Dow, Charles (IE), 357 Edgerton, David (BE), 624 Fairbank, Richard (IE), 717 Fisher, Sir Ronald (IE), 167, 333, 365 Galton, Sir Francis (BE), 151, 154 Gates, Bill (IE), 92

Gosset, William S (BE), 233; (IE), 332–336, 368 Gretzky, Wayne (E), 125, 126

Hathway, Jill (EIA), 168 Howe, Gordie (E), 125 Ibuka, Masaru (IE), 769 Jones, Edward (IE), 357 Juran, Joseph (IE), 770 Kellogg, John Harvey and Will Keith (IE), 531 Kendall, Maurice (BE), 821

Laplace, Pierre-Simon (IE), 286–287 Legendre, Adrien-Marie (BE), 151 Likert, Rensis (IE), 807 Lockhart, Denis (BE), 670 Lowe, Lucius S (IE), 137 Lowell, James Russell (IE), 365 MacArthur, Douglas (IE), 770 MacDonald, Dick and Mac (BE), 624 Mann, H B (BE), 810

Martinez, Pedro (E), 656 McGwire, Mark (E), 126 McLamore, James (BE), 624 Morita, Akio (IE), 769 Morris, Nigel (IE), 717 Obama, Michelle (JC), 680 Patrick, Deval (E), 326 Pepys, Samuel (IE), 770 Roosevelt, Franklin D (IE), 305 Sagan, Carl (IE), 374 Sammis, John (IE), 836–837 Sarasohn, Homer (IE), 770, 771 Savage, Sam (IE), 226 Shewhart, Walter A (IE), 771, 772, 794 Simes, Trisha (EIA), 168

Spearman, Charles (IE), 163, 823 Starr, Cornelius Vander (IE), 85 Street, Picabo (IE), 646, 648 Taguchi, Genichi, 165 Tillman, Bob (IE), 138 Tukey, John W (IE), 100, 417 Tully, Beth (EIA), 116–117 Twain, Mark (IE), 450 Whitney, D R (BE), 810 Wilcoxon, Frank (BE), 809 Williams, Venus and Serena (E), 242 Zabriskie, Dave (IE), 548

Finance and Investments

Annuities (E), 483 Assessing Risk (E), 393, 483; (IE), 189–190, 315 Assessing Risk (E), 75

Blue Chip Stocks (E), 861 Bonds (E), 483; (IE), 357–358 Brokerage Firms (E), 478, 483; (EIA), 18 CAPE10 (BE), 256; (IE), 246; (P), 269 Capital Investments (E), 75 Currency (BE), 673–674, 677, 680; (E), 49, 272, 273,

301, 324; (IE), 12–13 Dow Jones Industrial Average (BE), 248; (E), 181; (GE), 454; (IE), 357–359, 365, 451–452

Financial Planning (E), 22–23 Gold Prices (IE), 194 Growth and Value Stocks (E), 436–437; (P), 237–238 Hedge Funds (IE), 449–450

Investment Analysts and Strategies (BE), 226; (E), 326, 483; (GE), 283–284; (P), 322

401(k) Plans (E), 213 London Stock Exchange (IE), 332 Managing Personal Finances (EIA), 319 Market Sector (IE), 549

Moving Averages (BE), 673–674; (E), 704; (IE), 671–673 Mutual Funds (E), 23, 125, 130, 132, 134, 176, 183, 272–273, 274, 275, 392, 397–398, 436–437, 443,

522, 526, 528, 612–613, 714, 861; (EIA), 697; (IE),

2, 12; (P), 174, 237–238 NASDAQ (BE), 671; (IE), 106, 450 NYSE (IE), 106, 109–110, 245–246, 450 Portfolio Managers (E), 396, 436–437 Public vs Private Company (BE), 624; (IE), 331–332 Stock Market and Prices (E), 23, 77, 209, 273, 274,

295, 392, 396, 704–706; (GE), 103–104; (IE), 12, 86–88, 90–91, 93–94, 102–103, 109–111, 115,

146, 192, 195, 306, 358–359, 671–672, 675, 677–680; (JC), 193, 420; (P), 173–174, 322, 701 Stock Returns (E), 275, 396, 443, 486, 526, 612–613, 761; (IE), 450; (P), 121

Stock Volatility (IE), 86–88, 105, 248 Student Investors (E), 297, 298, 393 Sustainable Stocks (E), 439 Trading Patterns (E), 478; (GE), 454–456; (IE), 94, 110, 450–452, 458

Venture Capital (BE), 234 Wall Street (IE), 450 Wells Fargo/Gallup Small Business Index (E), 75 Wilshire Index (E), 526, 612–613

Cereal (BE), 401; (E), 436, 658–659, 758, 764–765, 829–830; (GE), 252–254; (IE), 260, 532–534 Coffee (E), 178, 707; (EIA), 116–117; (JC), 281 Company Cafeterias and Food Stations (E), 351; (JC),

408, 415 Farmed Salmon (BE), 336, 344 Fast Food (E), 22, 48, 482, 616; (IE), 624–625; (P), 44 Food Consumption and Storage (E), 133; (GE), 65; (JC),

408, 415 Food Prices (E), 705, 707

Ithaca Times (IE), 115

Learning Disabilities (EIA), 18

Literacy and Illiteracy Rates (E), 184, 616

MBAs (E), 22, 79, 391, 396

National Assessment in Education Program (E), 441

National Center for Education Statistics (E), 395

Online Education (EIA), 425

Rankings of Business Schools (E), 184

Reading Ability and Height (IE), 148

Retention Rates (E), 327

Stanford University (IE), 226

Statistics Grades (IE), 457

Test Scores (BE), 250–252; (E), 22, 130, 207, 270, 274,

437, 441, 524–525, 528, 759, 829; (JC), 243, 249

Traditional Curriculums (E), 435

University at California Berkeley (BE), 68; (E), 83

Energy

Batteries (E), 240, 354, 524

Energy and the Environment (E), 210; (IE), 138

Energy Use (E), 528–529; (P), 322

Fuel Economy (E), 22, 49, 129, 177, 394, 441, 479,

Green Energy and Bio Fuels (E), 439

Heat for Homes (GE), 636–640

Oil (E), 76, 527, 713–715, 859–860; (IE), 535–537

Organization of Petroleum Exporting Countries (OPEC)

(E), 527, 714–715

Renewable Energy Sources (P), 560

Wind Energy (E), 355, 445; (IE), 542–543; (P), 560

Environment

Acid Rain (E), 437

Atmospheric Levels of Carbon Dioxide (E), 520

Clean Air Emissions Standards (E), 328, 396–397

Conservation Projects (EIA), 41

El Niño (E), 185

Emissions/Carbon Footprint of Cars (E), 180, 395, 833

Environmental Causes of Disease (E), 439

Environmental Defense Fund (BE), 336

Environmental Groups (E), 325

Environmental Protection Agency (BE), 336; (E), 22, 48,

180, 271, 530, 833; (EIA), 557; (IE), 138

Environmental Sustainability (E), 528–529

Global Warming (E), 47, 207–208, 438; (P), 517

Greenhouse Gases (E), 185, 517

Hurricanes (E), 132, 438, 567

Long-Term Weather Predictions (E), 208

Ozone Levels (E), 129, 530

Pollution Control (E), 212, 328, 354, 395, 611, 764

Streams and the Environment (EIA), 41

Toxic Waste (E), 48

Ethics

Bias in Company Research and Surveys (E), 45–49;

(EIA), 41, 69; (IE), 4, 37–39

Bossnapping (E), 322; (GE), 313–314; (JC), 314

Business Ethics (E), 326, 396

Corporate Corruption (E), 324, 483

Employee Discrimination (E), 83, 479, 480, 762; (EIA),

602, 751

False Claims (EIA), 235

Housing Discrimination (E), 48, 485

Index of Applications xxvii

U.S Fish and Wildlife Service (E), 47 U.S Food and Drug Administration (E), 799; (EIA), 557 U.S Geological Survey (BE), 545

U.S Securities and Exchange Commission (IE), 449; (P), 174

Zoning Laws (IE), 309

Human Resource Management/Personnel

Assembly Line Workers (E), 438 Employee Athletes (E), 446 Flexible Work Week (BE), 818 Full-Time vs Part-Time Workers (E), 75 Hiring and Recruiting (E), 50, 75, 324, 328; (IE),

466, 532 Human Resource Accounting (IE), 807 Human Resource Data (E), 45–46, 49–50, 210, 485;

(IE), 807 Job Interviews (E), 239 Job Performance (E), 176; (IE), 40, 69 Job Satisfaction (E), 49, 242, 275, 324, 439–440, 486, 831

Mentoring (E), 483 Promotions (E), 241 Ranking by Seniority (IE), 14 Rating Employees (JC), 420 Relocation (E), 214–215 Shifts (E), 763 Staff Cutbacks (E), 75; (IE), 38 Testing Job Applicants (E), 390, 438 Training (E), 270, 761

Worker Productivity (E), 132–133, 446, 763 Workforce Size (IE), 107–108

Working Parents (E), 81

Insurance

Allstate Insurance Company (E), 301 Auto Insurance and Warranties (E), 209–210, 301, 325, 478

Fire Insurance (E), 209 Health Insurance (E), 46, 50, 82–83, 213, 327, 329; (IE), 865; (JC), 582; (P), 475

Hurricane Insurance (E), 242 Insurance Company Databases (BE), 149, 372; (E), 82–83; (IE), 114; (JC), 17, 24

Insurance Costs (BE), 372; (E), 75; (IE), 220–225 Insurance Profits (E), 128, 240; (GE), 340–341, 369–370; (IE), 85, 223, 342, 368 Life Insurance (E), 574, 657–658; (IE), 217–221, 223–225

Medicare (E), 394 National Insurance Crime Bureau (E), 180 Online Insurance Companies (E), 444–445, 831 Property Insurance (GE), 340–341, 369–370; (JC), 17 Sales Reps for Insurance Companies (BE), 342; (E), 183; (GE), 340–341, 369–370; (IE), 342, 368 Tracking Insurance Claims (E), 180; (P), 856–857 Travel Insurance (GE), 846–848; (P), 856–857

Management

Data Management (IE), 8, 15–16 Employee Management (IE), 69 Hotel Management (BE), 624 Management Consulting (E), 210 Management Styles (E), 486 Marketing Managers (E), 213, 526, 759, 762; (P),

Manufacturing

Adhesive Compounds, 799 Appliance Manufacturers (E), 395, 610 Assembly Line Production (BE), 624 Camera Makers (E), 862 Car Manufacturers (E), 214, 325, 391, 478, 481, 759; (IE), 772

Ceramics (E), 178 Clothing (E), 299–300 Computer and Computer Chip Manufacturers (E), 242,

396, 803; (IE), 774–775, 848 Cooking and Tableware Manufacturers (IE), 491–492 Dental Hygiene Products (EIA), 168

Drug Manufacturers (E), 212–213, 276, 393 Exercise Equipment (E), 446

Injection Molding (E), 761 Manufacturing Companies and Firms (E), 486, 763 Metal Manufacturers (GE), 501; (IE), 491–492; (P), 388 Product Registration (E), 324; (IE), 31, 37

Prosthetic Devices (GE), 786–789 Silicon Wafer (IE), 334, 774–775, 779, 790–792 Stereo Manufacturers (GE), 257–259 Tire Manufacturers (E), 209, 276, 398, 446–447 Toy Manufacturers (E), 75, 82, 209; (IE), 770 Vacuum Tubes (IE), 770

Marketing

Chamber of Commerce (IE), 309 Direct Mail (BE), 316; (E), 325; (EIA), 877; (GE), 725–726, 745–748; (IE), 316, 720, 740–742, 863–864; (P), 388

International Marketing (E), 45, 74–77, 82, 212–213; (GE), 65–66, 198–200; (IE), 58–63, 67 Market Demand (E), 48, 78, 212–213; (GE), 34–35; (IE), 306, 315–316; (P), 322

Marketing Costs (E), 78 Marketing New Products (E), 391, 396, 437; (GE), 198–200; (IE), 316

Marketing Slogans (E), 443 Marketing Strategies (E), 212–213, 238; (GE), 725–726; (IE), 466–470, 534, 720

Market Research (E), 45–46, 75–76, 83, 210–211, 324,

351, 392, 440; (EIA), 472; (GE), 65–66, 405–407; (IE), 26–27, 29, 32, 58–59, 62, 305, 720; (P), 44 Market Share (E), 74–75

Online Marketing (E), 241; (IE), 720 Public Relations (E), 396 Rating Products (EIA), 69 Researching Buying Trends (E), 213, 438–439; (GE), 413–415; (IE), 2, 14, 31, 52, 200, 315–316, 411,

417, 719; (JC), 204, 234; (P), 206 Researching New Store Locations (E), 22, 32; (JC), 234 Web-Based Loyalty Program (P), 476

Media and Entertainment

The American Statistician (E), 444 Applied Statistics (E), 444 British Medical Journal (E), 481, 832

Hot Dogs (E), 434–435

Ice Cream Cones (E), 177

Irradiated Food (E), 325

Milk (E), 393–394, 799; (IE), 770; (JC), 408

Nuts (E), 479

Opinions About Food (E), 482; (GE), 65–66; (IE), 58–63;

(JC), 471; (P), 44

Oranges (E), 574

Organic Food (E), 22, 435, 439, 829; (EIA), 41, 383, 825

Pet Food (E), 81–82; (IE), 770

Pizza (E), 126, 179, 442, 655–657; (IE), 543–545; (P),

Computer Games (E), 295, 482, 568

Dice (E), 241, 478; (IE), 285–286

City Council (E), 325–326

European Union (IE), 16

Fair and Accurate Credit Transaction Act (IE), 190

Food and Agriculture Organization of the United Nations

(E), 133

Government Agencies (E), 567, 834; (EIA), 268;

(IE), 16, 85

Immigration Reform (E), 482–483

Impact of Policy Decisions (IE), 4

International Trade Agreements (E), 328

IRS (E), 211, 327, 355

Jury Trials (BE), 362; (E), 395; (IE), 361–363, 372, 378

Labor Force Participation Rate (E), 444

Labor Productivity and Costs (E), 525

Labor Unions (E), 662; (EIA), 751

Minimum Wage (E), 81

National Center for Productivity (E), 132–133

Protecting Workers from Hazardous Conditions (E), 759

Right-to-Work Laws (E), 614

Sarbanes Oxley (SOX) Act of 2002 (E), 483

U.S Department of Commerce (IE), 464

U.S Department of Labor (E), 81

Health and Education Levels (E), 758–759 Health Benefits of Fish (E), 482 Hearing Aids (E), 760 Heart Disease (E), 75; (EIA), 168; (IE), 92 Hepatitis C (E), 80

Herbal Compounds (E), 22, 438 Hormones (GE), 818–820 Hospital Charges and Discharges (E), 82–83 Lifestyle and Weight (IE), 255

Medical Tests and Equipment (EIA), 602; (IE), 377; (JC), 582

Mercury Levels in Fish (EIA), 557 Number of Doctors (IE), 148 Nutrition Labels (E), 616, 658–659; (EIA), 557; (IE), 532–534, 624–625

Orthodontist Costs (E), 238 Patient Complaints (E), 804 Pharmaceutical Companies (E), 326–327, 392, 568 Placebo Effect (E), 388, 391; (IE), 727

Public Health Research (IE), 719 Respiratory Diseases (E), 75 Side Effects of a Drug (E), 242 Skin Care (IE), 458–460 Smoking and Smoke-Free Areas (E), 298, 299, 565 Snoring (E), 214

Strokes (EIA), 168 Teenagers and Dangerous Behaviors (E), 393, 832; (IE), 67 Vaccinations (E), 323

Vision (E), 298 Vitamins (E), 275, 326–327; (IE), 2 World Health Organization (IE), 16

Politics

2008 Elections (E), 396 Candidates (BE), 362 Election Polls (E), 22, 47, 326, 525; (IE), 25, 29, 37, 307–308

Governor Approval Ratings (E), 329 Political Parties (E), 208, 242; (EIA), 877 Readiness for a Woman President (E), 328, 525, 708 Truman vs Dewey (IE), 25, 37

Popular Culture

Attitudes on Appearance (GE), 462–463, 467–469;

(IE), 458-460, 466-470 Cosmetics (E), 212–213; (IE), 466–470 Fashion (E), 481–483; (IE), 51, 55; (P), 206 Pets (E), 81–82; (IE), 718–719; (JC), 719, 722, 728, 737 Playgrounds (E), 48

Religion (E), 46 Roller Coasters in Theme Parks (IE), 617–622, 627–631 Tattoos (E), 80

Titanic, sinking of (E), 479–480, 485, 862

Quality Control

Cuckoo Birds (E), 832 Food Inspection and Safety (E), 48, 49, 50, 212–213, 323; (EIA), 825; (GE), 65–66; (IE), 30

Product Defects (E), 178, 209, 239, 240, 241, 325, 328,

391, 478, 801–803; (IE), 770; (JC), 197; (P), 388 Product Inspections and Testing (E), 130, 207, 212, 242–243, 272, 275, 297–298, 325, 393, 394, 437, 446–447, 478, 527, 757–758, 764–766, 829; (IE),

165, 166, 262, 332–333, 617, 772, 848–852; (P), 756 Product Ratings and Evaluations (E), 22, 391, 392, 438, 655–657, 862; (IE), 618

Product Recalls (E), 241

Product Reliability (E), 480–481, 801, 862; (IE), 165–166, 618

Repair Calls (E), 240 Six Sigma (E), 800; (IE), 794 Taste Tests (E), 655–657, 758; (IE), 727 Warranty on a Product (E), 862

Home Buyers (IE), 39, 577 Home Improvement and Replacement (IE), 138, 142–143, 149–150

Home Ownership (E), 396 Home Sales and Prices (BE), 106–107, 108, 641; (E), 77–78, 130–131, 133, 177, 207, 210, 212–214, 241–242, 355, 446, 521, 570, 610, 611–612; (GE), 587–591, 598–600, 636–640; (IE), 203, 577–585, 591–595, 601, 635, 641–642; (P), 294, 349, 431, 826

Home Size and Price (E), 181; (GE), 160–162 Home Values (E), 295, 301, 352, 443, 446, 523; (GE), 587–591; (IE), 577–578

Housing Development Projects (EIA), 853 Housing Industry (E), 355, 443; (EIA), 853; (JC), 339 Housing Inventory and Time on Market (E), 132, 391; (GE), 598–600

MLS (E), 132 Mortgage Lenders Association (E), 298 Multi-Family Homes (E), 78 Real Estate Websites (IE), 577–578 Renting (E), 485

Standard and Poor’s Case-Shiller Home Price Index (E), 133

Zillow.com real estate research site (GE), 587; (IE), 577–578

Salary and Benefits

Assigned Parking Spaces (JC), 471 Companionship and Non-medical Home Services (EIA), 512

Day Care (E), 323, 324 Employee Benefits (E), 213, 327, 395 Executive Compensation (E), 177, 300–301, 351–352; (IE), 112–113, 267, 337–338

Hourly Wages (E), 526, 762 Pensions (IE), 218 Raises and Bonuses (E), 23–24, 762; (IE), 12–13 Salaries (BE), 114; (E), 75, 175, 177, 179, 180, 608–610, 612, 759; (EIA), 602, 751; (IE), 457, 601 Training and Mentorship Programs (EIA), 512 Worker Compensation (E), 323

Sales and Retail

Air Conditioner Sales (E), 177 American Girl Sales (E), 75 Book Sales and Stores (E), 175, 176, 177, 478, 482 Campus Calendar Sales (E), 78

Car Sales (E), 125, 132, 177, 179, 323 Catalog Sales (BE), 598; (E), 22, 324; (EIA), 472; (IE),

729, 731–732; (JC), 680

Broadway and Theater (E), 24, 79, 608–610, 662

Business Week (E), 22, 74, 124; (IE), 7, 450

Cartoons (IE), 36, 40, 68, 227, 310

Chance (E), 22, 444, 485, 832–833

Chicago Tribune (IE), 25

CNN (E), 324; (EIA), 168; (IE), 305

The Economist (BE), 461

Errors in Media Reporting (IE), 25

Financial Times (E), 22, 704; (IE), 1

Forbes (E), 134; (IE), 107, 548

Fortune (BE), 114; (E), 22, 74, 486, 526; (IE), 25, 108;

(P), 856

Globe & Mail (GE), 818

The Guardian (E), 322

Journal of Applied Psychology (E), 440

Sports Illustrated (BE), 461

Television (E), 48, 272, 326, 393; (IE), 37, 38, 148–149

Theme Parks (E), 24, 48

Binge Drinking (E), 299

Blood Pressure (E), 75, 212–213, 568; (IE), 146

Blood Type (E), 211, 241; (GE), 231–232; (IE), 260–261

Body Fat Percentages (E), 568; (JC), 582

Cancer (E), 75, 482; (EIA), 168; (IE), 167

Centers for Disease Control and Prevention (E), 75, 565;

(IE), 67

Cholesterol (E), 212–213, 276, 295–296

Colorblindness (E), 570

Cranberry Juice and Urinary Tract Infections (E), 481

Dental Care (EIA), 168

Diabetes (EIA), 168

Drinking and Driving (E), 47

Drug Tests and Treatments (E), 393, 438, 832; (IE), 2,

400; (JC), 363

Freshman 15 Weight Gain (E), 830–831

Genetic Defects (E), 299

Gum Disease (EIA), 168

Index of Applications xxix

Service Industries and Social Issues

Advocacy Groups (EIA), 425 American Association of Retired People (E), 329 American Heart Association (IE), 532 American Red Cross (E), 211, 242; (GE), 231–232; (IE), 260–261

Charities (E), 329; (IE), 378; (P), 349–350 Firefighters (IE), 167

Fundraising (E), 296, 297 Nonprofit and Philanthropic Organizations (E), 131, 275,

296, 297, 329, 396, 398; (GE), 231–232; (IE), 16,

28, 51–52, 807, 863–864; (P), 349–350, 652 Paralyzed Veterans of America (IE), 28, 863–864; (P), 652

Police (E), 324, 354–355, 480, 608–610 Service Firms (E), 486

Football (E), 179, 525; (IE), 54, 317 Golf (E), 127–128, 392, 442–443; (P), 606 Hockey (E), 125–126

Indianapolis 500 (E), 23 Kentucky Derby (E), 23, 129 NASCAR (E), 272 Olympics (E), 76, 442, 758; (IE), 646–648 Pole Vaulting (E), 799

Running (E), 442; (IE), 548 Sailing (E), 830 Skiing (E), 394, 704; (IE), 646–648 Super Bowl (IE), 54

Swimming (E), 272, 442, 758 Tennis (E), 242, 275 World Series (E), 179

Surveys and Opinion Polls

Company Surveys (E), 45, 324, 327, 481–482, 486 Consumer Polls (E), 45–50, 208, 212–213, 241, 325,

328, 395, 481–483, 483–484, 760; (EIA), 41, 472;

(GE), 467; (IE), 26, 31, 35–39, 305, 808; (JC), 30,

34, 204, 234, 281; (P), 44, 206 Cornell National Social Survey (CNSS) (BE), 493–494, 504 Gallup Polls (BE), 463–464, 466, 470; (E), 22, 47–49,

81, 210, 295, 326, 393, 525, 708; (IE), 16, 25–26, 307–308; (P), 322

International Polls (E), 326, 482; (GE), 462–463; (IE),

305, 458–460, 466–470 Internet and Email Polls (E), 22–23, 47, 122, 123, 212,

323, 324–325, 328, 760; (EIA), 319, 472; (GE), 198;

(JC), 30; (P), 44 Mailed Surveys (BE), 316; (E), 45, 325; (GE), 198; (IE),

37, 316 Market Research Surveys (E), 45–50, 78, 82–83, 210–211, 324, 392, 861; (GE), 34, 65–66, 198; (IE), 26–27, 29, 32, 39, 58, 64; (P), 44

Newspaper Polls (E), 48, 329 Public Opinion Polls (BE), 317; (E), 48, 50, 75–76, 81, 210–213, 299, 324–325, 486; (GE), 65–66, 313–314; (IE), 25–26, 30, 32, 36, 58–63, 307–308;

(JC), 314, 420

Student Surveys (E), 22–23, 214, 326, 481; (GE), 34–35; (IE), 2, 14, 18; (JC), 420, 471 Telephone Surveys (E), 22, 47–49, 210–211, 213, 272,

295, 296, 328, 393, 396, 493–494; (GE), 198; (IE), 25–27, 29, 37–39, 194, 317; (JC), 233

Technology

Cell Phones (E), 49, 177, 213, 243, 272, 275, 295, 354, 565–566, 575, 759; (IE), 39, 162–163, 164, 365 Compact Discs (E), 328; (IE), 13

Computers (BE), 13; (E), 47, 75, 211, 295, 323, 353,

437, 529–530, 862; (GE), 221–222; (IE), 29, 31; (P), 798; (TH), 43, 71–72

Digital music (E), 327; (IE), 13 Downloading Movies or Music (BE), 89, 105, 111; (E),

122, 326, 328; (IE), 343 DVDs (E), 800; (IE), 835–836 E-Mail (E), 211, 241, 325 Flash Drives (IE), 69 Hard Drives (E), 175, 176 Help Desk (E), 239; (IE), 837–839, 844–846 Impact of the Internet and Technology on Daily Life (E), 486

Information Technology (E), 211, 391, 435, 482, 485; (IE), 718; (P), 856–857

Inkjet Printers (E), 527 Internet Access (BE), 720; (E), 48, 79, 327, 441, 760 iPods and MP3 Players (E), 49, 128, 296, 324, 396; (IE), 848; (JC), 98, 197

LCD screens (BE), 262; (E), 242 Managing Spreadsheet Data (TH), 20 Multimedia Products (IE), 835–836 Online Journals or Blogs (E), 486 Personal Data Assistant (PDA) (E), 858–859; (IE), 848 Personal Electronic Devices (IE), 848

Product Instruction Manuals (E), 393; (IE), 32 Software (E), 47, 77, 239; (EIA), 69; (IE), 13, 18, 115,

343, 397 Technical Support (IE), 835–837, 852–853 Telecommunications (BE), 453, 458; (E), 210; (IE), 837 Web Conferencing (E), 75

Transportation

Air Travel (BE), 470–471, 837, 841; (E), 50, 209, 239,

272, 322, 353, 391, 396, 566–567, 571, 661, 711–713, 859–860; (EIA), 649; (IE), 39, 67, 278; (JC), 34, 375; (P), 388, 476

Border Crossings (BE), 670, 687, 690 Cars (BE), 403, 691; (E), 24, 177, 184, 214, 239–240,

325, 392, 441, 480–481, 523, 528, 759, 765–766; (EIA), 877; (GE), 814–816; (IE), 532, 634–635, 643, 808–809, 813–814

Commuting to Work (E), 239–240; (JC), 266 Motorcycles (E), 24, 185–186, 615–616, 660–662 National Highway Transportation Safety Administration (BE), 140; (E), 765–766

Seatbelt Use (BE), 850, 852; (E), 275, 389 Texas Transportation Institute (IE), 139 Traffic Accidents (BE), 140, 142, 147, 149, 160 Traffic and Parking (E), 241, 323, 352, 354–355, 655–657

Traffic Congestion and Speed (E), 271, 274, 298; (IE),

139, 140, 142 Travel and Tourism (E), 238, 295, 711, 714; (EIA), 649 U.S Bureau of Transportation Statistics (E), 353 U.S Department of Transportation (BE), 670; (E), 126–127, 353, 396

Closing (E), 242

Clothing Stores (BE), 361, 363, 365, 373, 581; (E), 201

Coffee Shop (E), 45, 178, 478; (EIA), 116–117; (JC),

234, 281

Comparing Sales Across Different Stores (E), 436

Computer Outlet Chain (E), 296; (JC), 152

Department Store (E), 81–82, 481–483; (P), 206

Expansions (E), 123; (IE), 137–138

Food Store Sales (BE), 288; (E), 22, 123–124, 128, 240,

325, 435; (EIA), 41, 383

Frequent Shoppers (E), 481–483

Friendship and Sales (GE), 413–415, 812–813; (IE),

Monthly Sales (E), 704

Music Stores (E), 122; (IE), 729, 731–732, 735–736

National Indicators and Sales (E), 22

New Product Sales (IE), 400

Number of Employees (JC), 152, 159

Optometry Shop (JC), 64

Paper Sales (IE), 69

Predicted vs Realized Sales (E), 24; (EIA), 602

Promotional Sales (E), 22, 209, 392; (GE), 405–407,

409–411; (IE), 200, 400–401, 409

Quarterly Sales and Forecasts (BE), 686; (E), 22, 704,

707, 709–710, 714; (GE), 681–684, 692–695; (IE),

2, 666, 671, 685–692; (P), 701–702

Regional Sales (BE), 452; (E), 180

Retail and Wholesale Sales (E), 49

Retail Price (E), 573, 575; (GE), 501–503; (IE), 492–493,

505–507, 509–510; (JC), 556; (P), 516

Sales Costs and Growth (E), 78; (IE), 14

Sales Representatives (E), 479; (EIA), 602

Seasonal Spending (E), 530, 707, 709–710; (GE),

421–423; (IE), 668, 686–687

Secret Sales (E), 209

Shelf Location and Sales (E), 437, 764–765; (IE), 2, 534

Shopping Malls (IE), 32, 35, 39; (JC), 234

Store Policies (EIA), 292

Toy Sales (E), 75, 82, 107–108

U.S Retail Sales and Food Index (E), 612, 614

Video Store (IE), 35

Weekly Sales (E), 22, 435, 442; (IE), 543–545

Yearly Sales (E), 571, 704; (IE), 14

Science

Aerodynamics (IE), 30

Biotechnology Firms (E), 325

Chemical Industry (E), 325

Chemicals and Congenital Abnormalities (E), 394

Cloning (E), 325

Cloud Seeding (E), 444

Contaminants and Fish (BE), 336; (E), 50

Gemini Observatories (E), 800

Genetically Engineered Foods (IE), 2

IQ Tests (E), 271, 273, 274, 758; (IE), 166

Metal Alloys (IE), 491

Psychology Experiments (BE), 727; (E), 758

Research Grant Money (EIA), 18

Soil Samples (E), 48

Space Exploration (E), 49

Temperatures (E), 185; (IE), 14, 750

Testing Food and Water (E), 48, 437, 522

Units of Measurement (E), 49; (IE), 12, 14n, 547

Statistics and

Variation

Just look at a page from the Financial Times

website, like the one shown here It’s full of

“statistics.” Obviously, the writers of the

Financial Times think all this

information is important, but isthis what Statistics is all about?Well, yes and no This pagemay contain a lot of facts, but aswe’ll see, the subject is muchmore interesting and rich thanjust spreadsheets and tables

“Why should I learn Statistics?”you might ask “After all, I don’tplan to do this kind of work Infact, I’m going to hire people to

do all of this for me.” That’s fine.But the decisions you make based

on data are too important todelegate You’ll want to be able

to interpret the data thatsurrounds you and to come toyour own conclusions And you’llfind that studying Statistics ismuch more important andenjoyable than you thought

1.1 So, What Is Statistics?

It seems every time we turn around, someone is collecting data on us, from everypurchase we make in the grocery store to every click of our mouse as we surf theWeb The United Parcel Service (UPS) tracks every package it ships from oneplace to another around the world and stores these records in a giant database Youcan access part of it if you send or receive a UPS package The database is about

17 terabytes—about the same size as a database that contained every book in theLibrary of Congress would be (But, we suspect, not quite as interesting.) What cananyone hope to do with all these data?

Statistics plays a role in making sense of our complex world Statisticians assessthe risk of genetically engineered foods or of a new drug being considered by theFood and Drug Administration (FDA) Statisticians predict the number of newcases of AIDS by regions of the country or the number of customers likely to re-spond to a sale at the supermarket And statisticians help scientists, social scientists,and business leaders understand how unemployment is related to environmentalcontrols, whether enriched early education affects the later performance of schoolchildren, and whether vitamin C really prevents illness Whenever you have dataand a need to understand the world, you need Statistics

If we want to analyze student perceptions of business ethics (a question we’llcome back to in a later chapter), should we administer a survey to every single uni-versity student in the United States—or, for that matter, in the world? Well, thatwouldn’t be very practical or cost effective What should we do instead? Give upand abandon the survey? Maybe we should try to obtain survey responses from asmaller, representative group of students Statistics can help us make the leap fromthe data we have at hand to an understanding of the world at large We talk aboutthe specifics of sampling in Chapter 3, and the theme of generalizing from the spe-cific to the general is one that we revisit throughout this book We hope this text

will empower you to draw conclusions from data and make valid business decisions

in response to such questions as:

• Do university students from different parts of the world perceive business ethicsdifferently?

• What is the effect of advertising on sales?

• Do aggressive, “high-growth” mutual funds really have higher returns than moreconservative funds?

• Is there a seasonal cycle in your firm’s revenues and profits?

• What is the relationship between shelf location and cereal sales?

• How reliable are the quarterly forecasts for your firm?

• Are there common characteristics about your customers and why they chooseyour products?—and, more importantly, are those characteristics the same amongthose who aren’t your customers?

Our ability to answer questions such as these and draw conclusions from

data depends largely on our ability to understand variation That may not be the

term you expected to find at the end of that sentence, but it is the essence ofStatistics The key to learning from data is understanding the variation that is allaround us

Data vary People are different So are economic conditions from month tomonth We can’t see everything, let alone measure it all And even what we domeasure, we measure imperfectly So the data we wind up looking at and basing ourdecisions on provide, at best, an imperfect picture of the world Variation lies at theheart of what Statistics is all about How to make sense of it is the central challenge

of Statistics

“It is the mark of a truly

intelligent person to be moved by

statistics.”

Q: What is Statistics?

A: Statistics is a way of reasoning, along

with a collection of tools and methods,

designed to help us understand the

world.

Q: What are statistics?

A: Statistics (plural) are quantities

calculated from data.

Q: So what is data?

A: You mean, “what are data?” Data is

the plural form The singular is datum.

Q: So, what are data?

A: Data are values along with

their context.

How Will This Book Help? 3

1.2 How Will This Book Help?

A fair question Most likely, this book will not turn out to be what you expect Itemphasizes graphics and understanding rather than computation and formulas.Instead of learning how to plug numbers in formulas you’ll learn the process ofmodel development and come to understand the limitations both of the data youanalyze and the methods you use Every chapter uses real data and real businessscenarios so you can see how to use data to make decisions

Graphs

Close your eyes and open the book at random Is there a graph or table on thepage? Do it again, say, ten times You probably saw data displayed in many ways,even near the back of the book and in the exercises Graphs and tables help youunderstand what the data are saying So, each story and data set and every newstatistical technique will come with graphics to help you understand both the methodsand the data

Process

To help you use Statistics to make business decisions, we’ll lead you through theentire process of thinking about a problem, finding and showing results, and tellingothers what you have discovered The three simple steps to doing Statistics for

business right are: Plan, Do, and Report.

Plan first Know where you’re headed and why Clearly defining and

understand-ing your objective will save you a lot of work

Do is what most students think Statistics is about The mechanics of calculating

statistics and making graphical displays are important, but the computations are usually the least important part of the process In fact, we usually turn thecomputations over to technology and get on with understanding what the resultstell us

Report what you’ve learned Until you’ve explained your results in a context that

someone else can understand, the job is not done

“Get your facts first, and then

you can distort them as much as

you please (Facts are stubborn,

but statistics are more pliable.)”

—MARKTWAIN

At the end of most sections, we present a short example to help you put what you’velearned to immediate use After reading the example, try the corresponding end-of-section exercises at the end of the chapter These will help prepare you for theother exercises that tend to use all the skills of the chapter

Each chapter applies the new concepts taught in worked examples called Guided

Examples These examples model how you should approach and solve problems

using the Plan, Do, Report framework They illustrate how to plan an analysis, theappropriate techniques to use, and how to report what it all means These step-by-step examples show you how to produce the kind of solutions and case studyreports that instructors and managers or, better yet, clients expect to see You willfind a model solution in the right-hand column and background notes and discus-sion in the left-hand column

PLAN

DO

REPORT

Sometimes, in the middle of the chapter, you’ll find sections called Just Checking,

which pose a few short questions you can answer without much calculation Usethem to check that you’ve understood the basic ideas in the chapter You’ll find theanswers at the end-of-chapter exercises

Statistics often requires judgment, and the decisions based on statistical analysesmay influence people’s health and even their lives Decisions in government canaffect policy decisions about how people are treated In science and industry, inter-pretations of data can influence consumer safety and the environment And inbusiness, misunderstanding what the data say can lead to disastrous decisions Thecentral guiding principle of statistical judgment is the ethical search for a trueunderstanding of the real world In all spheres of society it is vitally important that

a statistical analysis of data be done in an ethical and unbiased way Allowingpreconceived notions, unfair data gathering, or deliberate slanting to affect statisticalconclusions is harmful to business and to society

At various points throughout the book, you will encounter a scenario under the

title Ethics in Action in which you’ll read about an ethical issue Think about the

issue and how you might deal with it Then read the summary of the issue and one solution to the problem, which follow the scenario We’ve related the ethicalissues to guidelines that the American Statistical Association has developed.1Thesescenarios can be good topics for discussion We’ve presented one solution, but weinvite you to think of others

One of the interesting challenges of Statistics is that, unlike some math and sciencecourses, there can be more than one right answer This is why two statisticians cantestify honestly on opposite sides of a court case And it’s why some people think thatyou can prove anything with statistics But that’s not true People make mistakes usingstatistics, and sometimes people misuse statistics to mislead others Most of themistakes are avoidable We’re not talking about arithmetic Mistakes usually involveusing a method in the wrong situation or misinterpreting results So each chapter has

a section called What Can Go Wrong? to help you avoid some of the most common

mistakes that we’ve seen in our years of consulting and teaching experience

“Far too many scientists have

only a shaky grasp of the

statistical techniques they are

using They employ them as

an amateur chef employs a

cookbook, believing the recipes

will work without understanding

why A more cordon bleu

attitude might lead to fewer

statistical soufflés failing to rise.”

template provided by the Guided Examples And they provide an opportunity to

practice reporting your conclusions in written form to refine your communicationskills where statistical results are involved Data sets for these case studies can befound on the disk included with this text

1 http://www.amstat.org/about/ethicalguidelines.cfm

At the end of each section, you’ll find a larger project that will help you integrateyour knowledge from the entire section you’ve been studying These more open-ended projects will help you acquire the skills you’ll need to put your knowledge towork in the world of business.

Although we show you all the formulas you need to understand the calculations,you will most often use a calculator or computer to perform the mechanics of astatistics problem And the easiest way to calculate statistics with a computer is with

a statistics package Several different statistics packages are used widely Althoughthey differ in the details of how to use them, they all work from the same basicinformation and find the same results Rather than adopt one package for thisbook, we present generic output and point out common features that you shouldlook for We also give a table of instructions to get you started on four packages:Excel, Minitab, SPSS, and JMP Instructions for Excel 2003 and DataDesk can befound on the CD accompanying this textbook

You’ll find all sorts of stuff in

margin notes, such as stories and

quotations For example:

“Computers are useless They

can only give you answers.”

While Picasso underestimated the

value of good statistics software, he

did know that creating a solution

requires more than just Doing—it

means you have to Plan and

Report, too!

From time to time we’ll take time out to discuss an interesting or important side issue.

We indicate these by setting them apart like this.2

At the end of each chapter, you’ll see a brief summary of the chapter’s learning

ob-jectives in a section called What Have We Learned? That section also includes a list of the Terms you’ve encountered in the chapter You won’t be able to learn the

material from these summaries, but you can use them to check your knowledge ofthe important ideas in the chapter If you have the skills, know the terms, and un-derstand the concepts, you should be well prepared—and ready to use Statistics!

Exercises

Beware: No one can learn Statistics just by reading or listening The only way tolearn it is to do it So, at the end of each chapter (except this one) you’ll find

Exercises designed to help you learn to use the Statistics you’ve just read about.

Some exercises are marked with a red You’ll find the data for these exercises onthe book’s website, www.aw-bc.com/sharpe or on the book’s disk, so you can usetechnology as you work the exercises

We’ve structured the exercises so that the end-of-section exercises are foundfirst These can be answered after reading each section After that you’ll find end-of-chapter exercises, designed to help you integrate the topics you’ve learned in thechapter We’ve also paired up and grouped the exercises, so if you’re having troubledoing an exercise, you’ll find a similar exercise either just before or just after it.You’ll find answers to the odd-numbered exercises at the back of the book Butthese are only “answers” and not complete solutions What’s the difference? Theanswers are sketches of the complete solutions For most problems, your solution

T

2 Or in a footnote.

How Will This Book Help? 5

should follow the model of the Guided Examples If your calculations match the

numerical parts of the answer and your argument contains the elements shown inthe answer, you’re on the right track Your complete solution should explain thecontext, show your reasoning and calculations, and state your conclusions Don’tworry too much if your numbers don’t match the printed answers to every decimalplace Statistics is more than computation—it’s about getting the reasoningcorrect—so pay more attention to how you interpret a result than to what the digit

in the third decimal place is

*Optional Sections and Chapters

Some sections and chapters of this book are marked with an asterisk (*) These areoptional in the sense that subsequent material does not depend on them directly

We hope you’ll read them anyway, as you did this section

Getting Started

It’s only fair to warn you: You can’t get there by just picking out the highlightedsentences and the summaries This book is different It’s not about memorizingdefinitions and learning equations It’s deeper than that And much more interest-ing But

You have to read the book!

Data

Amazon.comAmazon.com opened for business in July 1995, billingitself even then as “Earth’s Biggest Bookstore,” with anunusual business plan: They didn’t plan to turn a profitfor four to five years Although some shareholderscomplained when the dotcom bubble burst, Amazoncontinued its slow, steady growth, becoming profitablefor the first time in 2002 Since then, Amazon hasremained profitable and has continued to grow By

2004, they had more than 41 million active customers in

over 200 countries and were ranked the

74th most valuable brand by Business Week.

Their selection of merchandise hasexpanded to include almost anything youcan imagine, from $400,000 necklaces, toyak cheese from Tibet, to the largest book

in the world In 2008, Amazon.com soldnearly $20 billion worth of products onlinethroughout the world

Amazon R&D is constantly monitoringand evolving their website to best servetheir customers and maximize their salesperformance To make changes to thesite, they experiment by collectingdata and analyzing what works best

As Ronny Kohavi, former director ofData Mining and Personalization,said, “Data trumps intuition Instead

of using our intuition, we experiment

on the live site and let our customerstell us what works for them.”

Amazon.com has recently stated “many of the importantdecisions we make at Amazon.com can be made with data

There is a right answer or a wrong answer, a better answer or

a worse answer, and math tells us which is which These are our favorite kinds of decisions.”1While we might prefer thatAmazon refer to these methods as Statistics instead of math, it’sclear that data analysis, forecasting, and statistical inference arethe core of the decision-making tools of Amazon.com

Many years ago, stores in small towns knew their customers personally

If you walked into the hobby shop, the owner might tell you about anew bridge that had come in for your Lionel train set The tailorknew your dad’s size, and the hairdresser knew how your mom likedher hair There are still some stores like that around today, but we’re increasinglylikely to shop at large stores, by phone, or on the Internet Even so, when youphone an 800 number to buy new running shoes, customer service representativesmay call you by your first name or ask about the socks you bought six weeks ago

Or the company may send an e-mail in October offering new head warmers forwinter running This company has millions of customers, and you called withoutidentifying yourself How did the sales rep know who you are, where you live, andwhat you had bought?

The answer to all these questions is data Collecting data on their customers,transactions, and sales lets companies track inventory and know what theircustomers prefer These data can help them predict what their customers may buy

in the future so they know how much of each item to stock The store can use thedata and what they learn from the data to improve customer service, mimicking thekind of personal attention a shopper had 50 years ago

2.1 What Are Data?

Businesses have always relied on data for planning and to improve efficiency andquality Now, more than ever before, businesses rely on the information in data tocompete in the global marketplace Most modern businesses collect information onvirtually every transaction performed by the organization, including every itembought or sold These data are recorded and stored electronically, in vast digital

repositories called data warehouses.

In the past few decades these data warehouses have grown enormously in size,but with the use of powerful computers, the information contained in them is ac-cessible and used to help make decisions, sometimes almost instantaneously Whenyou pay with your credit card, for example, the information about the transaction istransmitted to a central computer where it is processed and analyzed A decisionwhether to approve or deny your purchase is made and transmitted back to thepoint of sale, all within a few seconds

“Data is king at Amazon.

Clickstream and purchase data

are the crown jewels at Amazon.

They help us build features to

personalize the website

What Are Data? 9

Companies use data to make decisions about other aspects of their business aswell By studying the past behavior of customers and predicting their responses,they hope to better serve their customers and to compete more effectively This

process of using data, especially of transactional data (data collected for recording

the companies’ transactions) to make other decisions and predictions, is sometimes

called data mining or predictive analytics The more general term business

analytics (or sometimes simply analytics) describes any use of statistical analysis to

drive business decisions from data whether the purpose is predictive or simplydescriptive

Leading companies are embracing business analytics Richard Fairbank, theCEO and founder of Capital One, revolutionized the credit card industry byrealizing that credit card transactions hold the key to understanding customerbehavior Reed Hastings, a former computer science major, is the founder andCEO of Netflix Netflix uses analytics on customer information both to recom-mend new movies and to adapt the website that customers see to individual tastes.Netflix offered a $1 million prize to anyone who could improve on the accuracy oftheir recommendations by more than 10% That prize was won in 2009 by a team

of statisticians and computer scientists using predictive analytics and data-miningtechniques The Oakland Athletics use analytics to judge players instead of thetraditional methods used by scouts and baseball experts for over a hundred years

The book Moneyball documents how business analytics enabled them to put

together a team that could compete against the richer teams in spite of the severelylimited resources available to the front office

To understand better what data are, let’s look at some hypothetical companyrecords that Amazon might collect:

105-9318443- 0198646

105-1872500-N B000068ZV Q

Bad Blood Nashville Katherine H.

Try to guess what these data represent Why is that hard? Because these data

have no context Whether the data are numerical (consisting only of numbers),

alphabetic (consisting only of letters), or alphanumerical (mixed numbers and letters),they are useless unless we know what they represent Newspaper journalists know

that the lead paragraph of a good story should establish the “Five W’s”: who, what, when, where, and (if possible) why Often, we add how to the list as well Answering

these questions can provide a context for data values and make them meaningful.

The answers to the first two questions are essential If you can’t answer who and what, you don’t have data, and you don’t have any useful information.

We can make the meaning clear if we add the context of who the data are

about and what was measured and organize the values into a data table such as