Statistics in Action

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Statistics in Action

Understanding a World of Data

Second Edition

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Project Editor Josephine Noah

Consulting Editor Kendra Lockman

Project Administrators Elizabeth Ball, Aaron Madrigal Editorial Assistants Aneesa Davenport, Nina Mamikunian

Teacher Consultant and Writer Corey Andreasen, North High School, Sheboygan, Wisconsin Mathematics Reviewer and Cindy Clements, Trinidad State Junior College, Trinidad, Colorado Accuracy Checker

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AP Teacher Reviewers Angelo DeMattia, Columbia High School, Maplewood, New Jersey

Beth Fox-McManus, formerly of Alan C. Pope High School, Marietta, Georgia Dan Johnson, Silver Creek High School, San Jose, California

Multicultural Reviewers Gil Cuevas, University of Miami, Coral Gables, Florida Genevieve Lau, Skyline College, San Bruno, California

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iii

About the Authors

Ann E. Watkins is Professor of Mathematics at California State University, Northridge (CSUN). She received her Ph.D. in education from the University of California, Los Angeles. She is a former president of the Mathematical Association of America (MAA) and a Fellow of the American Statistical Association (ASA). Dr. Watkins has served as co-editor of College Mathematics Journal, as a member

of the Board of Editors of American Mathematical Monthly, and as Chair of the

Advanced Placement Statistics Development Committee. She was selected as the 1994–1995 CSUN Outstanding Professor and won the 1997 CSUN Award for the Advancement of Teaching Effectiveness. Before moving to CSUN in 1990, she taught for the Los Angeles Unified School District and at Pierce College in Los Angeles. In addition to numerous journal articles, she is the co-author of books based on work produced by the Activity-Based Statistics Project (co-authored with Richard Scheaffer), the Quantitative Literacy Project, and the Core-Plus Mathematics Project. Richard L. Scheaffer is Professor Emeritus of Statistics at the University of

Florida, where he served as chairman of the Department of Statistics for 12 years. He received his Ph.D. in statistics from Florida State University. Dr. Scheaffer’s research interests are in the areas of sampling and applied probability, especially in their applications to industrial processes. He has published numerous papers and is co-author of four college-level textbooks.

In recent years, much of his effort has been directed toward statistics education at the elementary, secondary, and college levels. He was one of the developers of the Quantitative Literacy Project where he helped form the basis of the data analysis emphasis in mathematics curriculum standards recommended by the National Council of Teachers of Mathematics. Dr. Scheaffer has also directed the task force that developed the Advanced Placement Statistics Program and served as its first Chief Faculty Consultant. Dr. Scheaffer is Fellow and past president of the American Statistical Association, from which he received a Founders Award.

George W. Cobb is the Robert L. Rooke Professor of Statistics at Mt. Holyoke College, where he served a three-year term as Dean of Studies. He received his Ph.D. in statistics from Harvard University, and is an expert in statistics education with a significant publication record. He chaired the joint committee on undergraduate statistics of the Mathematical Association of America and the American Statistical Association and is a Fellow of the American Statistical Association. He also led the Statistical Thinking and Teaching Statistics (STATS) project of the Mathematical Association of America, which helped professors of mathematics learn to teach statistics. Over the past two decades, Dr. Cobb has frequently served as an expert witness in lawsuits involving alleged employment discrimination.

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iv

Acknowledgments

This book is a product of what we have learned from the statisticians and teachers who have been actively involved in helping the introductory statistics course evolve into one that emphasizes activity-based learning of statistical concepts while reflecting modern statistical practice. This book is written in the spirit of the recommendations from the MAA’s STATS project, the ASA’s Quantitative Literacy and GAISE projects, and the College Board’s AP Statistics course. We hope that it adequately reflectsS the wisdom and experience of those with whom we have worked and who have inspired and taught us.

We owe special thanks to Corey Andreasen, an outstanding high school mathematics and AP Statistics teacher at North High School in Sheboygan, Wisconsin, for his insight into what makes a topic “teachable” to high school students. His careful review of the manuscript led to many clarifications of wording and improvements in exercises, all of which will make it easier for you to learn the material. Corey also has made substantial contributions to the solutions and teacher’s notes, adding his unique perspective and sense of humor.

It has been an awesome experience to work with the Key Curriculum staff and field-test teachers, who always put the interests of students and teachers first. Their commitment to excellence has motivated us to do better than we ever could have done on our own. Steve, Casey, Jim, Kristin B., Kristin F., and the rest of the staff have been professional and astute throughout. Our deepest gratitude goes to Cindy Clements and Josephine Noah, the editors of the first and second editions, respectively, who have been a joy to work with. (Not all authors say that—and mean it—about their editors.) Cindy and Josephine were outstanding high school teachers before coming to Key. Their organizational skills, experience in the classroom, and insight have improved every chapter of this text.

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< NOBR>

A Note to Students from the Authors ix

Statistical Reasoning: Investigating a

Claim of Discrimination

2

1.1 Discrimination in the Workplace: Data Exploration 4 1.2 Discrimination in the Workplace: Inference 11

Chapter Summary 20

Exploring Distributions

26

2.1 Visualizing Distributions: Shape, Center, and Spread 28

2.2 Graphical Displays of Distributions 42

2.3 Measures of Center and Spread 56

2.4 Working with Summary Statistics 74

2.5 The Normal Distribution 83

Chapter Summary 95

Relationships Between Two Quantitative Variables

104

3.1 Scatterplots 106

3.2 Getting a Line on the Pattern 116

3.3 Correlation: The Strength of a Linear Trend 140 3.4 Diagnostics: Looking for Features That the Summaries Miss 162

3.5 Shape-Changing Transformations 179

Chapter Summary 203

Sample Surveys and Experiments

216

4.1 Why Take Samples, and How Not To 219

4.2 Random Sampling: Playing It Safe by Taking Chances 231 4.3 Experiments and Inference About Cause 243 4.4 Designing Experiments to Reduce Variability 262

Chapter Summary 278 CHAPTER

1

CHAPTER

2

CHAPTER

3

CHAPTER

4 Probability Models

286

5.1 Constructing Models of Random Behavior 288 5.2 Using Simulation to Estimate Probabilities 301 5.3 The Addition Rule and Disjoint Events 315

5.4 Conditional Probability 325

5.5 Independent Events 339

Chapter Summary 349

Probability Distributions

356

6.1 Random Variables and Expected Value 358

6.2 The Binomial Distribution 382

6.3 The Geometric Distribution 393

Chapter Summary 402

Sampling Distributions

408

7.1 Generating Sampling Distributions 410

7.2 Sampling Distribution of the Sample Mean 426 7.3 Sampling Distribution of the Sample Proportion 446

Chapter Summary 458

Inference for Proportions

466

8.1 Estimating a Proportion with Confidence 468

8.2 Testing a Proportion 490

8.3 A Confidence Interval for the Difference of Two Proportions 518 8.4 A Significance Test for the Difference of Two Proportions 526

8.5 Inference for Experiments 539

Chapter Summary 554

Inference for Means

560

9.1 A Confidence Interval for a Mean 562

9.2 A Significance Test for a Mean 580

9.3 When Things Aren’t Normal 602

9.4 Inference for the Difference Between Two Means 616

9.5 Paired Comparisons 640 Chapter Summary 663 CHAPTER

5

CHAPTER

6

CHAPTER

7

CHAPTER

8

CHAPTER

9

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Chi-Square Tests

672

10.1 Testing a Probability Model: The Chi-Square Goodness-of-Fit Test 674

10.2 The Chi-Square Test of Homogeneity 692

10.3 The Chi-Square Test of Independence 711

Chapter Summary 728

Inference for Regression

736

11.1 Variation in the Slope from Sample to Sample 739

11.2 Making Inferences About Slopes 755

11.3 Transforming for a Better Fit 771

Chapter Summary 790

Statistics in Action: Case Studies

798

12.1 Mum’s the Word! 800

12.2 Keeping Tabs on Americans 803

12.3 Baseball: Does Money Buy Success? 810

12.4 Martin v. Westvaco Revisited: Testing for Discrimination

Against Employees 817

Statistical Tables

824

Table A: Standard Normal Probabilities 824

Table B: t-Distribution Critical Values 826

Table C: Critical Values 827

Table D: Random Digits 828

Glossary 829

Brief Answers to Selected Problems 839

Index 883 Photo Credits 894 CHAPTER

10

CHAPTER

11

CHAPTER

12

APPENDIX vii

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ix Data enter the conversation whether you talk about income, sports, health, politics, the weather, or prices of goods and services. In fact, in this age of information technology, data come at you at such a rapid rate that you can catch only a glimpse of the masses of numbers. You cannot cope intelligently in this quantitative world unless you have an understanding of the basic concepts of statistics and have had practice making informed decisions using real data.

Statistics in Action is designed for students taking an introductory high

school statistics course and includes all of the topics in the Advanced Placement (AP) Statistics syllabus. Beginning in Chapter 1 with a court case about age discrimination, you will be immersed in real-world problems that can be solved only with statistical methods. You will learn to explore, summarize, and display data; design surveys and experiments; use probability to understand random behavior; make inferences about populations by looking at samples from those populations; and make inferences about the effect of treatments from designed experiments.

After completing your statistics course, you will be prepared to take the AP Statistics Exam, to take a follow-up college-level course, and, above all, to make informed decisions in this world of data.

You will be using this book in a first course in statistics, so, you aren’t required to know anything yet about statistics. You may find that your success in statistics results more from your perseverance in trying to understand what you read rather than your skill with algebra. However, basic topics from algebra, such as slope, linear equations, exponential equations, and the idea of a logarithm, will arise throughout the book. Be prepared to review these as you go along.

Statistics from a Modern Perspective

Statistical work is more interactive than it was a generation ago. Computers and graphing calculators have automated the graphical exploration of data and, in the process, have made the practice of statistics a more visual enterprise. Statistical techniques are also changing as simulations allow statisticians (and you) to shift the emphasis from following recipes for calculations to paying more attention to statistical concepts. Your instructor has selected this book so that you may learn this modern, data-analytic approach to statistics and because he or she encourages you to be an active participant in the classroom, wants you to see real data (if you have only pretend data, you can only pretend to analyze it), believes that statistical analyses must be tailored to the data, and uses graphing calculators or statistical software for data analysis and for simulations.

A Note to Students

from the Authors

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x

These features grow out of the vigorous changes that have been reshaping the practice of statistics and the teaching of statistics over the last quarter century.

The most basic question to ask about any data set is, “Where did the data come from?” Good data for statistical analysis must come from a good plan for data collection. Thus, Statistics in Action treats the design and analysis of experiments honestly and thoroughly and discusses how these methods of collecting data differ from observational studies. It then follows through on this theme by relating the statistical analysis to the manner in which the data were collected.

What You Should Know About This Book

Throughout Statistics in Action you will see many graphical displays, lots of real data, activities that introduce each major topic, computer printouts, questions for you to discuss with your class, and practice problems so that you can be sure you understand the basics before you move on. The practice problems are found at the end of every section, organized by topic. You should work every problem for those topics that you wish to learn. The answers to all practice problems and odd-numbered exercises are in the back of the book.

Also in the back of the book you will find a glossary of statistical notation, a glossary of statistical terminology, statistical tables, and an index so that you can locate topics quickly. Two tables are reprinted inside the back cover of the book.

You should keep in mind that the emphasis throughout is on the development of statistical concepts. Even when looking for a numerical answer in a practice problem or exercise, think about the underlying concept that is being illustrated. Concepts are carefully developed in the written text, so read the material thoughtfully and strive to fully understand what is being explained. Concepts are also developed in the discussion questions. These are designed for in-class discussions, but regard them as part of your reading assignment, even if class time is too limited for full discussion. Through reading, discussing, and working practice problems and exercises you will develop a profound understanding of statistical thinking, and that will serve you well as a basis for a lifetime of coping with a quantitative world.

Ann Watkins Dick Scheaffer George Cobb

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1

Statistical Reasoning:

Investigating a Claim

of Discrimination

Were older workers discriminated against during a company’s downsizing? When an older worker felt he had been unfairly laid off, his lawyers called on a statistician to help them evaluate the claim.

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In the year Robert Martin turned 54, the Westvaco Corporation, which makes paper products, decided to downsize. They laid off several members of the engineering department, including Robert Martin. Later that year, he sued Westvaco, claiming he had been laid off because of his age. A major piece of Martin’s case was based on a statistical analysis of the ages of the Westvaco employees.

In the two sections of this chapter, you will get a chance to try your hand at two very different kinds of statistical work, exploration and inference. Exploration is an informal, open-ended examination of data. Your goal in the first section will be to uncover and summarize patterns in data from Westvaco that bear on the Martin case. You will try to formulate and answer basic questions such as “Were those who were laid off older on average than those who weren’t laid off?” You can use any tools—graphs, averages, and so on—that you think might be useful. Inference, which you’ll use in the second section, is quite different from exploration in that it follows strict rules and focuses on judging whether the patterns you found are the sort you would expect. You’ll use inference to decide whether the patterns you find in the Westvaco data are the sort you would expect from a company that does not discriminate on the basis of age, or whether further investigation into possible age discrimination is needed.

The purpose of this first chapter is to familiarize you with the ideas of statistical thinking before you involve yourself with the details of statistical methods. It is easy to get caught in the trap of doing rather than understanding, of asking how rather than why. You can’t do statistics unless you learn the methods, but you must not get so caught up in the details of the methods that you lose sight of what they mean. Doing and thinking, method and meaning, will compete for your attention throughout this course.

In this chapter, you will learn the basic ideas of

• exploring data—uncovering and summarizing patterns

• making inferences from data—deciding whether an observed feature of the data could reasonably be attributed to chance alone

These ideas will remain key components of the statistical concepts you’ll develop and study throughout this course.

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4 Chapter 1 Statistical Reasoning: Investigating a Claim of Discrimination

Discrimination in the Workplace: Data Exploration

Robert Martin was one of 50 people working in the engineering department of Westvaco’s envelope division. One spring, Westvaco’s management went through five rounds of planning for a reduction in their work force. In Round 1, they eliminated 11 positions, and they eliminated 9 more in Round 2. By the time the layoffs ended, after all five rounds, only 22 of the 50 workers had kept their jobs. The average age in the department had fallen from 48 to 46.

After Martin, age 54, was laid off, he sued Westvaco for age discrimination. Display 1.1 shows the data provided by Westvaco to Martin’s lawyers. The statistical analysis in the lawsuit used all 50 employees in the engineering department of the envelope division, with separate analyses for salaried and hourly workers. Each row in Display 1.1 corresponds to one worker, and each column corresponds to a characteristic of the worker: job title, whether hourly or salaried, month and year of birth, month and year of hire, and age at birthday in 1991. The next-to-last column (Round) tells how the worker fared in the downsizing: 1 means chosen for layoff in Round 1 of planning for the reduction in force, 2 means chosen in Round 2, and so on for Rounds 3, 4, and 5; 0 means “not chosen for layoff.”

The subjects (or objects) of statistical examination often are called cases. In the rows in Display 1.1, the cases are individual Westvaco employees. Their characteristics, in the columns, are the variables. If you pick a row and read across, you find information about a single case. (For example, Robert Martin, in Row 44, was salaried, was born in September 1937, was hired in October 1967, was chosen for layoff in Round 2, and turned 54 in 1991.) Although reading across might seem the natural way to read the table, in statistics you will often find it useful to pick a column and read down. This gives you information about a single variable as you range through all the cases. For example, pick Age, read down the column, and notice the variability in the ages. It is variability like this—the fact that individuals differ—that can make it a challenge to see patterns in data and figure out what they mean.

Imagine: If there had been no variability—if all the workers had been of just two ages, say, 30 and 50, and Westvaco had laid off all the 50-year-olds and kept all the 30-year-olds—the conclusion would be obvious and there would be no need for statistics. But real life is more subtle than that. The ages of the laid-off workers varied, as did the ages of the workers retained. Statistical methods were designed to cope with such variability. In fact, you might define statistics as the science of learning from data in the presence of variability.

Although the bare fact that the ages vary is easy to see in the data table, the pattern of those ages is not so easy to see. This pattern—what the values are and how often each occurs—is their distribution. In order to see that pattern, a graph is better than a table. The dot plot in Display 1.2 shows the distribution of the ages of the 36 salaried employees who worked in the engineering department just before the layoffs began.

[To learn how to create a dot plot on your calculator, see Calculator Note 1A.]

1.1

Variables provide information about cases.

Variability is what statistics is all about.

A distribution is a record of variability.

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Display 1.1 The data in Martin v. Westvaco. [ Source: Martin v. Envelope Division of Westvaco Corp., CA No. 92-03121-MAP, 850 Fed. Supp. 83 (1994).]

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Display 1.2 provides some useful information about the variability in the ages, but by itself doesn’t tell anything about possible age discrimination in the layoffs. For that, you need to distinguish between those salaried workers who lost their jobs and those who didn’t. The dot plot in Display 1.3, which shows those laid off and those retained, provides weak evidence for Martin’s case. Those laid off generally were older than those who kept their jobs, but the pattern isn’t striking.

Display 1.3 shows that most salaried workers who were laid off were age 50 or older. However, this alone doesn’t support Martin’s case because most of the workers were age 50 or older to begin with.

One way to proceed is to make a summary table. The table shown here classifies the salaried workers according to age and whether they were laid off or retained. (Using 50 as the dividing age between “younger” and “older” is a somewhat arbitrary, but reasonable, decision.)

To decide whether Martin has a case, compare the proportion of salaried workers under 50 who were laid off (6 out of 16, or 0.375) with the proportion of those 50 or older who were laid off (12 out of 20, or 0.60). These proportions are quite different, an argument in favor of Martin.

Looking at the layoffs of the salaried workers round by round provides further evidence in favor of Martin. The dot plots in Display 1.4 show the ages of the salaried workers laid off and retained by round. These new dot plots use different

Display 1.2 Ages of the salaried workers. (Each dot represents a worker; the age is shown by the position of the dot along the scale below it.)

Display 1.3 Salaried workers: ages of those laid off and those retained.

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1.1 Discrimination in the Workplace: Data Exploration 7

symbols for laid-off workers and for retained workers. For example, in the top dot plot, the open circles represent the salaried workers whose jobs did not survive Round 1. In this round, the four oldest workers were laid off, but only one worker under age 50 was laid off. In the second round, one of the two oldest employees was laid off. But then this pattern stopped. Again, the evidence favors Martin but is far from conclusive.

You might feel as if the analysis so far ignores important facts, such as worker qualifications. That’s true. However, the first step is to decide whether, based on the data in Display 1.1, older workers were more likely to be laid off. If not, Martin’s case fails. If so, it is then up to Westvaco to justify its actions.

Exploring the Martin v. Westvaco Data

D1. Suppose you were the judge in the Martin v. Westvaco case. How would you use the information in Display 1.1 to decide whether Westvaco tended to lay off older workers in disproportionate numbers (for whatever reason)? D2. Display 1.5 (on the next page) is like Display 1.3 except that it gives data

for the hourly workers. Compare the plots for the hourly and salaried workers. Which provides stronger evidence in support of Martin’s claim of age discrimination?

Display 1.4 Salaried workers: ages of those laid off (open circles) and those retained (solid dots) in each round.

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D3. Whenever you think you have a message from data, you should be careful not to jump to conclusions. The patterns in the Westvaco data might be “real”—they reflect age discrimination on the part of management. On the other hand, the patterns might be the result of chance—management wasn’t discriminating on the basis of age but simply by chance happened to lay off a larger percentage of older workers. What’s your opinion about the Westvaco data: Do the patterns seem “real”—too strong to be explained by chance?

D4. The analysis up to this point ignores important facts such as worker qualifications. Suppose Martin makes a convincing case that older workers were more likely to be laid off. It is then up to Westvaco to justify its actions. List several specific reasons Westvaco might give to justify laying off a disproportionate number of older workers.

Summary 1.1: Data Exploration

Data exploration, or exploratory analysis, is a purposeful investigation to find patterns in data, using tools such as tables and graphs to display those patterns and statistical concepts such as di

Display 1.5 Hourly workers: ages of those laid off and those retained.

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1.1 Discrimination in the Workplace: Data Exploration 9

Practice problems help you master basic concepts and computations. Throughout this textbook, you should work all the practice problems for each topic you want to learn. The answers to all practice problems are given in the back of the book.

Exploring the Martin v. Westvaco Data

P1. Construct a dot plot similar to Display 1.3, comparing the ages of hourly workers who lost their jobs during Rounds 1–3 to the ages of hourly workers who still had their jobs at the end of Round 3. How do the ages differ? P2. This summary table classifies the hourly

workers according to age and whether they were laid off or retained.

a. What proportion of hourly workers under age 50 were laid off? Were not laid off?

b. What proportion of laid-off hourly workers were under age 50? Were age 50 or older?

c. What two proportions should you compute and compare in order to decide whether older hourly workers were disproportionately laid off? Make these computations and give your conclusion. d. Compare this table with the table for the

salaried workers on page 6. For which

group does the evidence more strongly favor Martin’s case?

P3. Display 1.6 shows layoffs and retentions by round for hourly workers. (There is no plot for Round 5 because no hourly workers were chosen for layoff in that round.) Compare the pattern for the hourly workers with the pattern for the salaried workers in Display 1.4 on page 7. For which group does the evidence more strongly favor Martin’s case?

Practice

Exercises

E1. This summary table classifies salaried workers as to whether they were laid off and their age, this time using 40 as the cutoff between younger and older workers.

a. What proportion of workers age 40 or older were laid off? What proportion of laid-off workers were age 40 or older? b. What proportion of workers under age 40

were laid off? What proportion were not laid off?

c. What two proportions should you compute and compare in order to

Display 1.6 Hourly workers: ages of those laid off (open circles) and those retained (solid dots) in each round.

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10 Chapter 1 Statistical Reasoning: Investigating a Claim of Discrimination

decide whether older workers were disproportionately laid off? Make these computations and give your conclusion. d. Compare this table with the table for the

salaried workers on page 6, where 50 was the age cutoff. If you were Martin’s lawyer, would you present a table using 40 or 50 as the cutoff?

E2. Explore whether hourly workers at Westvaco were more likely than salaried workers to lose their jobs.

a. Start by constructing a summary table to display the relevant data.

b. Compute two proportions that will allow you to make this comparison.

c. What do you conclude from comparing the proportions?

E3. Twenty-two workers kept their jobs. Explore whether the age distributions are similar for the hourly and salaried workers who kept their jobs.

a. Show the two age distributions on a pair of dot plots that have the same scale. How do these distributions differ?

b. Do your dot plots in part a support a claim that Westvaco was more inclined to keep older workers if they were salaried rather than hourly?

E4. Consider these three facts from your work in this section:

• Salaried workers were more likely to keep their jobs than were hourly workers. • Older workers were more likely to be laid

off than were younger workers. • Older workers were more likely to be

salaried than were younger workers. Putting these three facts together, what can you conclude? Is this evidence in favor of Martin’s case, or does it help Westvaco?

E5. Refer to Display 1.1 on page 5.

a. Create a summary table whose five cases are Round 1 through Round 5 and

whose three variables are total number of employees laid off in that round, number of employees laid off in that round who were 40 or older, and percentage laid off in that round who were 40 or older.

b. Describe any patterns you find in the table and what you think they might mean. E6. “Last hired, first fired” is shorthand for

“When you have to downsize, start by laying off the newest person, then the person hired next before that, and work back in reverse order of seniority.” (The person who’s been working longest will be the last to be laid off.) Examine the Westvaco data.

a. How was seniority related to the

decisions about layoffs in the engineering department at Westvaco?

b. What explanation(s) can you suggest for any patterns you find?

E7. Many tables in the media are arranged with cases as rows and variables as columns. For Displays 1.7 and 1.8 in parts a and b, identify the cases and the variables. Then compute the values missing from each table.

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E8. Suppose you are studying the effects of poverty and plan to construct a data set whose cases are the villages in Bolivia. Name some variables that you might study.

1.2 Discrimination in the Workplace: Inference 11

a.

b.

1.2 Discrimination in the Workplace: Inference

Overall, the exploratory work on the Westvaco data set in Section 1.1 shows that older workers were more likely than younger workers to be laid of and were laid off earlier. One of the main arguments in the court case, along the lines set out in D3, was about what those patterns mean: Can you infer from the patterns that Westvaco has some explaining to do, or are they the sort of patterns that tend to happen even in the absence of discrimination?

A comprehensive analysis of Martin v. Westvaco will have to wait for its reappearance among the case studies of Chapter 12, when you’ll be more familiar with the concepts and tools of statistics. For now, you can get a pretty good idea of how the analysis goes by working with a subset of the data.

Display 1.7 Number of bases stolen in a single season by the top five Major

League baseball players. [Source: mlb.mlb.com.]

Display 1.8 New York Stock Exchange Activity for October 29, 1929.

[Source: marketplace.publicradio.org.]

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The ages of the ten hourly workers involved in Round 2 of the layoffs, arranged from youngest to oldest, were 25, 33, 35, 38, 48, 55, 55, 55, 56, and 64. The three workers who were laid of were ages 55, 55, and 64. Display 1.9 shows these data on a dot plot.

To simplify the statistical analysis to come, it helps to “condense” the data into a single number, called a summary statistic. One possible summary statistic is the average, or mean, age of the three workers who lost their jobs:

Knowing what to make of the data requires balancing two points of view. On one hand, the pattern in the data is pretty striking. Of the five workers under age 50, all kept their jobs. Of the five who were 55 or older, only two kept their jobs. On the other hand, the number of workers involved is small: only three out of ten. Should you take seriously a pattern involving so few cases? Imagine two people taking sides in an argument that was at the center of the statistical part of the Martin case.

Martin: Look at the pattern in the data. All three of the workers laid

off were much older than the average age of all workers. That’s evidence of age discrimination.

Westvaco: Not so fast! You’re looking at only ten workers total, and only

three positions were eliminated. Just one small change and the picture would be entirely di erent. For example, suppose it had been the 25-year-old instead of the 64-year-old who was laid off. Switch the 25 and the 64, and you get a totally different set of averages. (Ages in red are those selected for layoff.)

Actual Data: 25 33 35 38 48 55 55 55 56 64

Altered Data: 25 33 35 38 48 55 55 55 56 64

Display 1.9 Hourly workers: ages of those laid off and those retained in Round 2.

Use a summary statistic to “condense” the data.

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1.2 Discrimination in the Workplace: Inference 13 See! Make just one small change, and the average age of the three who were laid off is lower than the average age of the others.

Martin: Not so fast yourself! Of all the possible changes, you picked the

one most favorable to your side. If you’d switched one of the 55-year-olds who got laid off with the 55-year-old who kept his or her job, the averages wouldn’t change at all. Why not compare what actually happened with all the possibilities?

Westvaco: What do you mean?

Martin: Start with the ten workers, and pick three at random. Do this over

and over, to see what typically happens, and compare the actual data with the results. then we’ll find out how likely it is that the average age of those laid off would be 58 or greater.

The dialogue between Martin and Westvaco describes one age-neutral method for choosing which workers to lay off: Pick three workers completely at random, with all sets of three having the same chance to be chosen.

Picking Workers at Random

D5. If you pick three of the ten ages at random, do you think you are likely to get an average age of 58 or greater?

D6. If the probability of getting an average age of 58 or greater turns out to be small, does this favor Martin or Westvaco?

Activity 1.2a shows you how to estimate the probability of getting an average age of 58 years or greater if you choose three workers at random. You will use simulation, a procedure in which you set up a model of a chance process (drawing three ages out of a box) that copies, or simulates, a real situation (selecting three employees at random to lay off).

By Chance or by Design?

What you’ll need: paper or 3 × 5 cards, a box or other container

Let’s test the process suggested by Martin’s advocate.

1. Create a model of a chance process. Write each of the ten ages on identical pieces of paper or 3 × 5 cards, and put the ten cards in a box. Mix them thoroughly, draw out three (the ones to be laid off), and record the ages. (continued) Simulation requires a

chance model.

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(continued)

2. Compute a summary statistic. Compute the average of the three numbers in your sample to one decimal place.

3. Repeat the process. Repeat steps 1 and 2 nine more times.

4. Display the distribution. Pool your results with the rest of your class and display the average ages on a dot plot.

5. Estimate the probability. Count the number of times your class computed an average age of 58 years or greater. Estimate the probability that simply by chance the average age of those chosen would be 58 years or greater. 6. Interpret your results. What do you conclude from your class’s estimate in

step 5?

[See Calculator Note 1B to learn how to do this kind of simulation with your calculator.]

Your simulation was completely age-neutral. All sets of three workers had exactly the same chance of being selected for layoff , regardless of age. The simulation tells you what results are reasonable to expect from that sort of age-blind process.

Shown here are the first 4 of 200 repetitions from such a simulation. (The ages in red are those selected for layoff .) The average ages of the workers selected for layoff —42.7, 48.0, 42.7, and 37.0—are highlighted by the red dots in the distribution of all 200 repetitions in Display 1.10.

Display 1.10 Results of 200 repetitions: the distribution of the average age of the three workers chosen for layoff by chance alone.

kind of data to expect if workers are selected at random for layoff.

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Out of 200 repetitions, only 10, or 5%, gave an average age of 58 or greater. So it is not at all likely that simply by chance you’d pick workers as old as the three Westvaco picked. Did the company discriminate? There’s no way to tell from the numbers alone—Westvaco might have a good explanation. On the other hand, if your simulation had told you that an average of 58 or greater is easy to get by chance alone, then the data would provide no evidence of discrimination and Westvaco wouldn’t need to explain.

To better understand how this logic applies to Martin v. Westvaco, imagine a realistic argument between the advocates for each side.

Martin: Look at the pattern in the data. All three of the workers laid off

were much older than average.

Westvaco: So what? You could get a result like that just by chance. If chance

alone can account for the pattern, there’s no reason to ask us for any other explanation.

Martin: Of course you could get this result by chance. The question is

whether it’s easy or hard to do so. If it’s easy to get an average as large as 58 by drawing at random, I’ll agree that we can’t rule out chance as one possible explanation. But if an average that large is really hard to get from random draws, we agree that it’s not reasonable to say that chance alone accounts for the pattern. Right?

Westvaco: Right.

Martin: Here are the results of my simulation. If you look at the three

hourly workers laid o in Round 2, the probability of getting an average age of 58 or greater by chance alone is only 5%. And if you do the same computations for the entire engineering department, the probability is a lot lower, about 1%. What do you say to that?

Westvaco: Well . . . I’ll agree that it’s really hard to get an average age that

extreme simply by chance, but that by itself still doesn’t prove discrimination.

Martin: No, but I think it leaves you with some explaining to do!

In the actual case, Martin and Westvaco reached a settlement out of court before the case went to trial.

The logic you’ve just seen is basic to all statistical inference, but it’s not easy to understand. In fact, it took mathematicians centuries to come up with the

ideas. It wasn’t until the 1920s that a brilliant British biological scientist and mathematician, R. A. Fisher, realized that results of agricultural experiments may be analyzed in a way similar to that in Activity 1.2a to see whether observed differences should be attributed to chance alone or to treatment. Calculus, in contrast, was first understood in 1665. Precisely because it is so important, the logic of using randomization as a basis for statistical inference will be seen over and over again throughout this book. You’ll have lots of time to practice with it.

Here we take a closer look at the logic.

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Key Steps in a Simulation

Here is a summary of the steps in a simulation:

1. Model. Set up a model in which chance is the only cause of being selected. In Activity 1.2a the model for an age-neutral chance process was to draw three numbers at random from the set of ten ages. You put the ages of the workers who could be laid off in a box and selected three by random draw. 2. Repetition. Repeat the process.

In the activity, you repeated the process of drawing ages many times. 3. Distribution. Display the distribution of the summary statistics, and

determine how likely the actual result or one even more extreme would be. In the activity, you used the average age to summarize the results, although other summary statistics also could be used. For each repetition, you computed the average age of those laid off and plotted that average on your dot plot. The simulation showed that the chance of an average age of 58 or greater was only about 0.05.

4. Conclusion. If the probability is small (and the definition of small will vary depending on the situation), conclude that some explanation other than just chance should be considered. If the probability isn’t small, conclude that you can reasonably attribute the result to chance alone.

In Round 2 of the Martin v. Westvaco case, the small probability of 0.05 (1 chance out of 20) meant that Westvaco had some explaining to do. However, the Round 2 evidence alone wouldn’t have been enough to serve as evidence of discrimination in a court case (which requires a probability of 0.025 or less).

The Logic of Inference

D7. Why must you estimate the probability of getting an average age of 58 or greater rather than the probability of getting an average age of 58?

D8. How unlikely is “too unlikely”? The probability you estimated in Activity 1.2a is in fact exactly equal to 0.05. In a typical court case, a probability of 0.025 or less is required to serve as evidence of discrimination.

a. Did the Round 2 layoffs of hourly workers in the Martin case meet the court requirement?

b. If the probability in the Martin case had been 0.01 instead of 0.05, how would that have changed your conclusions? 0.10 instead of 0.05? D9. A friend wants to bet with you on the outcome of a coin flip. The coin looks

fair, but you decide to do a little checking. You flip the coin, and it lands heads. You flip again—also heads. A third flip—heads. Flip—heads. Flip—heads. You continue and the coin lands heads 19 times in 20 flips. a. Explain why the evidence—19 heads in 20 flips—makes it hard to believe

the coin is fair.

b. Design and carry out a simulation to estimate how unusual this result would be if the coin were fair.

DISCUSSION

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Summary 1.2: Inference

Inference is a statistical procedure that involves deciding whether an event can reasonably be attributed to chance or whether you should look for—and perhaps investigate—some other explanation. In the Martin case, you used inference to determine whether the relatively high average age of the laid-off hourly employees in Round 2 could reasonably be due to chance.

Simulation is a useful device for inference.

• First, you set up a model of a process in which chance is the only factor influencing the outcomes.

• The next stage is repetition—you repeat the process.

• Then you plot the distribution of the summary statistics in order to determine how likely the actual result or one even more extreme would be.

• Finally, you reach—or infer—a conclusion. If the probability of getting a summary statistic as extreme as that from your actual data is small, conclude that chance isn’t a reasonable explanation. If the probability isn’t small, conclude that you can reasonably attribute the result to chance alone. In the Martin case, the probability was about 0.05, which was considered small enough to warrant asking for an explanation from Westvaco but not small enough to present in court as clear evidence of discrimination.

The Logic of Inference

P4. Suppose three workers were laid off from a set of ten whose ages were the same as those of the hourly workers in Round 2 in the Martin case. This time, however, the ages of those laid off were 48, 55, and 55.

25 33 35 38 48 55 55 55 56 64

a. Use the dot plot in Display 1.10 on page 14 to estimate the probability of getting an average age as large as or larger than that of those laid off in this situation. b. What would your conclusion be if

Westvaco had laid off workers of these three ages?

P5. At the beginning of Round 1, there were 14 hourly workers. Their ages were 22, 25, 33, 35, 38, 48, 53, 55, 55, 55, 55, 56, 59, and 64. After the layoffs were complete, the ages of those left were 25, 38, 48, and 56. Think about how you would repeat Activity 1.2a using these data.

a. What is the average age of the ten workers laid off?

b. Describe a simulation for finding the distribution of the average age of ten workers laid off at random.

c. The results of 200 repetitions from a simulation are shown in Display 1.11. Suppose 10 workers are picked at random for layoff from the 14 hourly workers. Make a rough estimate of the probability of getting, just by chance, the same or larger average age as that of the workers who actually were laid off (from part a). d. Does this analysis provide evidence in

Martin’s favor?

Practice

Display 1.11 Results of 200 repetitions.

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Exercises

E9. Revisit the idea of the simulation in Activity 1.2a, this time for all 14 hourly workers and using a different summary statistic. Use as your summary statistic the number of hourly workers laid off who were 40 or older. The ages listed here are those of the hourly workers, with the ages of those laid off in red. Note that, of the ten hourly workers laid off by Westvaco, seven were age 40 or older.

22 25 33 35 38 48 53 55 55 55 55 56 59 64

a. Write the 14 ages on 14 slips of paper and draw 10 at random to be chosen for layoff. How many of the 10 are age 40 or older?

b. The dot plot in Display 1.12 shows the results of 50 repetitions of this simulation. Estimate the probability that, by chance, seven or more of the ten hourly workers who were laid off would be age 40 or older.

c. Do you conclude that the proportion of laid-off workers age 40 or older could reasonably be due to chance alone, or should Westvaco be asked for an explanation?

E10. The ages of the ten hourly workers left after Round 1 are given here. The ages of the four workers laid off in Rounds 2 and 3 are shown in red. Their average age is 57.25.

25 33 35 38 48 55 55 55 56 64

a. Describe how to simulate the chance of getting an average age of 57.25 or more using the methods of Activity 1.2a. b. Perform your simulation once and

compute the average age of the four hourly workers laid off.

c. The dot plot in Display 1.13 shows the results of 200 repetitions of this simulation. What is your estimate of the probability of getting an average age as great as or greater than Westvaco did if four workers are picked at random for layoff in Rounds 2 and 3 from the ten hourly workers remaining after Round 1?

d. What is your conclusion?

E11. Mrs. Garcia was not happy when she found that her baker had raised the price of a loaf of bread—and she let him know it. However, she did buy her usual three loaves of bread. They seemed a little light, so she asked that they be weighed and that the other eight loaves the baker could have given her also be weighed. The other eight loaves weighed 14, 15, 15, 16, 16, 17, 17, and 18 ounces. The three loaves Mrs. Garcia was given weighed

Display 1.12 Results of 50 repetitions: the distribution of the number of workers age 40 or older from ten randomly selected workers.

Display 1.13 Results of 200 repetitions: mean ages of three randomly selected workers.

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14, 15, and 16 ounces. The baker claimed that he picked the three loaves at random. In Display 1.14, each dot represents the average weight of a random sample of three loaves.

Which of these conclusions should Mrs. Garcia draw?

A. Because the probability of getting an average weight as low as or lower than that of Mrs. Garcia’s three loaves is small, Mrs. Garcia should not be suspicious that the baker deliberately gave her lighter loaves. B. Because the probability of getting an

average weight as low as or lower than that of Mrs. Garcia’s three loaves is small, Mrs. Garcia should be suspicious that the baker deliberately gave her lighter loaves. C. Because the probability of getting an

average weight as low as or lower than that of Mrs. Garcia’s three loaves is fairly large, Mrs. Garcia should not be suspicious that the baker deliberately gave her lighter loaves.

D. Because the probability of getting an average weight as low as or lower than that

of Mrs. Garcia’s three loaves is fairly large, Mrs. Garcia should be suspicious that the baker deliberately gave her lighter loaves. E12. Snow in July? You have spent some time in

Oz. You think the date back in Kansas is July 4, but you can’t be sure because days might not have the same length in Oz as on Earth. A friendly tornado puts you and your dog Toto down in Kansas. However, you see snow falling (data). Which of these inferences should you make?

A. If this is Kansas, it is very unlikely to be snowing on July 4. Therefore, this probably isn’t Kansas.

B. If it is July 4, it is very unlikely to be snowing in Kansas. Therefore, this probably isn’t July 4.

C. If it is snowing in Kansas on July 4, it is time to go back to Oz.

D. If this is Kansas and it is July 4, it probably isn’t really snowing. E13. For some situations, instead of using

simulation, it is possible to find exact probabilities by counting equally likely outcomes. Suppose only two out of the ten hourly workers had been laid off in Round 2 and that those two workers were ages 55 and 64, with an average age of 59.5. It is straightforward, though tedious, to list all possible pairs of workers who might have been chosen. Here’s the beginning of a systematic listing. The first nine outcomes include the 25-year-old and one other. The next eight outcomes include the 33-year-old and one other, but not the 25-year-old

because the pair {25, 33} was already counted.

Display 1.14 Results of 200 repetitions: average

weight of three randomly selected loaves.

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a. How many possible pairs are there? (Don’t list them all!)

b. How many pairs give an average age of 59.5 or greater? (Do list them.)

c. If the pair is chosen completely at random, then all possibilities are equally likely and the probability of getting an average age of 59.5 or greater equals the number of pairs with an average of 59.5 or more divided by the total number of possible pairs. What is the probability? d. Is the evidence of age discrimination

strong or weak?

E14. In this exercise, you will follow the same steps as in E13 to find the probability of getting an average age of 58 or greater when drawing three hourly workers at random in

Round 2. The number of ways to pick three different workers from ten to lay off is

[See Calculator Note 1C to learn how to compute numbers of combinations.]

a. List the ways that give an average age of 58 or greater.

b. Compute the probability of getting an average age of 58 or greater when three workers are selected for layoff at random. c. How does this probability compare to the results of your class simulation in Activity 1.2a? Why do the two probabilities differ (if they do)?

Chapter Summary

In this chapter, you explored the data from an actual case of alleged age

discrimination, looking for evidence you considered relevant. You then saw how to use statistical reasoning to test the strength of the evidence: Are the patterns in the data solid enough to support Martin’s claim of age discrimination, or are they the sort that you would expect to occur even if there was no discrimination? Along the way you made a substantial start at learning many of the most

important statistical terms and concepts: distribution, cases and variables, summary statistic, simulation, and how to determine whether the result from the real-life situation can reasonably be attributed to chance alone or whether an explanation is called for.

You have practiced both thinking like a statistician and reporting your results like a statistician. Throughout this textbook, you will be asked to justify your answers in the real-world context. This includes stating assumptions, giving appropriate plots and computations, and writing a conclusion in context.

The last chapter of this book includes a final look at the Martin case.

E15. A teacher had two statistics classes, and students could enroll in either the earlier class or the later class. Final grades in the courses are given here.

Earlier class: 99 95 69 91 79 67 64 54 68 47 53 86 100 95 45 41 59 66 Later class: 84 68 94 77 88 75 88 91 83 61 97 75 37 82 62 49 43 93 Review Exercises

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Chapter Summary 21

a. Display these data on dot plots so that you can compare the two classes. b. What conclusion can you draw from the

dot plots? Could the difference between the two classes reasonably be attributed to chance, or do you think the teacher should look for an explanation? E16. Refer to the data in E15.

a. Make a table that divides the course grades into “fail” (less than 60) and “pass” (60 or more) for the two classes.

b. What proportion of the students in the earlier class passed? What proportion of students who passed were in the earlier class? What proportion of students passed overall?

c. What two proportions should you compute and compare in order to decide whether a disproportionate number of “passing” students enrolled in the earlier class? Make these computations and give your conclusion.

E17. This table classifies the Westvaco workers by whether they were laid off and whether they were under age 50 or were age 50 or older.

Choose the best conclusion to draw from this table.

A. The table supports a claim of age discrimination because most of the

people who were laid off were 50 or older. B. The table supports a claim of age

discrimination because a larger

percentage of the people age 50 or older were laid off than people under 50. C. The table supports a claim of age

discrimination because a larger percentage of the laid-off people were age 50 or older than were under 50. D. The table does not support a claim of age

discrimination because the number of people under age 50 who were laid off is

equal to the number of people age 50 or older who were retained.

E. The table does not support a claim of age discrimination because a larger percentage of people were 50 or older to begin with.

E18. Display 1.15 contains information about the planets in our solar system. The radius of each planet is given in miles, and the temperature is the average at the surface. What are the cases? What are the variables?

E19. Earlier you studied the summary table of salaried workers classi ed according to age and to whether they were laid off or retained, using 50 as the dividing age. That table is shown again here.

The proportion of those under age 50 who were laid off (6 out of 16, or 0.375) is smaller than the proportion of those age 50 or older who were laid off (12 out of 20, or 0.60). The key question, however, is “Is the actual 12 versus 6 split of those laid off consistent with selecting workers at random for layoff ?” a. Which of these demonstrations would

help Martin’s case?

A. Showing that it’s not unusual to get 12 or more older workers if 18 workers are selected at random for layoff

Display 1.15 Data about planets in our solar system.

[Source: solarsystem.nasa.gov.]

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B. Showing that it’s pretty unusual to get 12 or more older workers if 18 workers are selected at random for layoff b. To investigate this situation using

simulation, follow these steps once and record your results.

1. Make 36 identical white cards. Label 20 cards with an O for “50 or over” and 16 cards with a U for “under 50.” 2. Mix the cards in a bag and select the

18 to be laid off at random. 3. Count the number of O’s among the

18 selected.

c. Display 1.16 shows the results of a computer simulation of 200 repetitions conducted according to the rules given in part b. Where would your result from part b be placed on this dot plot?

d. From this simulation, a total of 26 out of 200 repetitions gave counts of “50 or older” that were 12 or more. What percentage of the repetitions is this? Is this percentage small enough to cast serious doubt on a claim that those laid off were chosen by chance?

E20. The Eastbanko Company had fifteen workers before laying off five of them. The ages of the fifteen workers were 22, 23, 25, 31, 34, 36,

37, 40, 41, 43, 44, 50, 55, 55, and 60, with the

ages of the five laid-off workers in bold. The dot plot in Display 1.17 gives the results of a simulation with 600 repetitions for the average age of five of these workers chosen for layoff at random. Each dot represents the average of five ages, rounded down to a whole number.

Display 1.16 Results of 200 repetitions: the distribution of the number of O’s in 18 randomly selected cards.

Display 1.17 Results of 600 repetitions: the distribution of the average age of five randomly chosen workers.

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Chapter Summary 23

a. Estimate the probability that if workers were selected by chance alone for layoff , the average age of those laid off would be as large as or larger than the average age of those in the actual layoffs.

b. If the 60-year-old sues for age discrimination, would Eastbanko have some explaining to do?

E21. In E9, you conducted a simulation to estimate the probability that, just by chance, seven or more of the ten hourly workers who were laid off would be age 40 or older. The ages of the fourteen hourly workers were 22, 25, 33, 35, 38, 48, 53, 55, 55, 55, 55, 56, 59, and 64.

a. How many ways can 10 workers be selected from 14 workers for layoff ? b. If there are a total of 10 layoff s, what

numbers of older workers would it have been possible to lay off ?

c. Using your calculator, find the number of ways that you can lay off

i. seven older workers and three younger workers

ii. eight older workers and two younger workers

iii. nine older workers and one younger worker

d. What is the probability that you will get 7 or more workers age 40 or older if you select 10 of the 14 workers completely at random for layoff ?

E22. Refer to your reasoning in E14, where you computed the probability that the three workers laid off in Round 2 would have an average age of 58 or greater. Describe how your reasoning and conclusions would be different if the workers’ ages were 25, 33, 35, 38, 48, 55, 55, 55, 55, and 55, and the three workers chosen for layoff were all age 55. Is the evidence stronger or weaker for Martin in this situation than in E14?

E23. The Society for the Preservation of Wild Gnus held a raffle last week and sold 50 tickets. The two lucky participants whose tickets were drawn received all-day passes to the Wild Gnu Park in Florida. But there was a near riot when the winners were announced—both winning tickets belonged to society president Filbert Newman’s cousins. After some intense questioning by angry ticket holders, it was determined that only 4 of the 50 tickets belonged to Newman’s cousins and the other 46 tickets belonged to people who were not part of his family. Newman’s final comment to the press was “Hey kids, I guess we were just lucky. Deal with it.”

One member of the Gnu Society was taking a statistics class and decided to deal with it by simulating the drawing. He put 50 tickets in a bowl; 4 of the tickets were marked “C” for “cousin” and 46 were marked “N” for “not a cousin.” The statistics student drew two tickets at random and kept track of the number of cousins picked. A er doing this 1000 times, the student found that 844 draws resulted in two N’s, 149 in one N and one C, and only 7 in two C’s.

a. Use the results of the simulation to estimate the probability that, in a fair drawing, both winning tickets would be held by Newman’s cousins.

b. Using the probability you estimated in part a, write a short paragraph that the statistics student can send to other members of the Gnu Society.

c. Is it possible that Newman’s cousins won the prizes by chance alone? Explain. d. Using reasoning like that in E13 and E14,

compute the exact probability that, in a fair drawing, both winning tickets would be held by Newman’s cousins.

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AP1. This plot shows the ages of the part-time and full-time students who receive financial aid at a small college. Which of the following is a conclusion about students at this college that cannot be drawn from the plot alone?

Part-time students who receive financial aid tend to be older than full-time students who receive financial aid. A larger proportion of part-time students than full-time students receive financial aid.

The oldest student receiving financial aid is a full-time student.

No student under age 18 receives financial aid.

More part-time students than full-time students receive financial aid. AP2. This table classifies hourly and salaried

workers as to whether they were laid off . Do the data support a claim that hourly workers are being treated unfairly?

Yes, because most people laid off were hourly workers.

Yes, because a bigger proportion of hourly workers were laid off than salaried workers.

No, because half of hourly workers were laid off and half were not.

No, because more than half of workers were hourly and less than half salaried.

No, because half of hourly workers were laid off, but more than half of salaried workers were laid off.

AP3. This table shows the number of male and female applicants who applied and were either admitted to or rejected from a graduate program. What proportion of admitted applicants were female?

AP4. For the data in AP3, in order to determine if there is evidence to continue investigating whether the graduate admissions process discriminates against females, a study takes a random sample of 25 out of the 70 applicants to be the “admitted” group. The proportion of females in the sample was computed. This process was repeated for a total of 50 random samples and the results are graphed below. What is the best conclusion to draw from this simulation?

The actual proportion of females among those admitted is very near the center of this distribution, so there is no evidence of discrimination.

The actual proportion of females among those admitted is very near the center of this distribution, so there is strong evidence of discrimination in favor of female applicants.