Book -- Mind on Statistics

Case Studies and Examples*
Chapter 1
Case Study 1.1
Case Study 1.2
Case Study 1.3
Case Study 1.4
Case Study 1.5
Case Study 1.6
Case Study 1.7

Who Are Those Speedy Drivers? 2
Safety in the Skies? 3
Did Anyone Ask Whom You’ve Been Dating? 3
Who Are Those Angry Women? 4
Does Prayer Lower Blood Pressure? 5
Does Aspirin Reduce Heart Attack Rates? 5
Does the Internet Increase Loneliness and Depression? 6

Chapter 2
Example 2.1 Seatbelt Use by Twelfth-Graders 19
Example 2.2 Lighting the Way to Nearsightedness 20
Example 2.3 Humans Are Not Good Randomizers 22
Example 2.4 Revisiting Nightlights and Nearsightedness 23
Example 2.5 Right Handspans 25
Example 2.6 Ages of Death of U.S. First Ladies 27
Example 2.7 Histograms for Ages of Death of U.S. First Ladies 30
Example 2.8 Big Music Collections 32
Example 2.9 Median and Mean Quiz Scores 37
Example 2.10 Median and Mean Number of CDs Owned 38
Example 2.11 Will “Normal” Rainfall Get Rid of Those Odors? 38

Example 2.12 Range and Interquartile Range for Fastest Speed Ever Driven 41
Example 2.13 Fastest Driving Speeds for Men 42
Example 2.14 Five-Number Summary and Outlier Detection for the Cambridge University
Crew Team 43
Example 2.15 Five-Number Summary and Outlier Detection for Music CDs 44
Example 2.16 Tiny Boatmen 48
Example 2.17 The Shape of British Women’s Heights 49
Example 2.18 Calculating a Standard Deviation 51
Example 2.19 Women’s Heights and the Empirical Rule 53

Chapter 3
Example 3.1 Do First Ladies Represent Other Women? 72
Example 3.2 Do Penn State Students Represent Other College Students? 72
Example 3.3 The Importance of Religion for Adult Americans 77
Example 3.4 Would You Eat Those Modified Tomatoes? 77
Example 3.5 Cloning Human Beings 78
Example 3.6 Representing the Heights of British Women 83
Example 3.7 A Los Angeles Times National Poll on the Millennium 88
Example 3.8 The Nationwide Personal Transportation Survey 89
Example 3.9 Which Scientists Trashed the Public? 92
Example 3.10 A Meaningless Poll 93
Example 3.11 Haphazard Sampling 94
Case Study 3.1 The Infamous Literary Digest Poll of 1936 94
Example 3.12 Laid Off or Fired? 96
Example 3.13 Most Voters Don’t Lie but Some Liars Don’t Vote 96
Example 3.14 Why Weren’t You at Work Last Week? 97
Example 3.15 Is Happiness Related to Dating? 98
Example 3.16 When Will Adolescent Males Report Risky Behavior? 98
Example 3.17 Politics Is All in the Wording 99
Example 3.18 Teenage Sex 100

Example 3.19 The Unemployed 100
Case Study 3.2 No Opinion of Your Own? Let Politics Decide 103

Chapter 4
Example 4.1 What Confounding Variables Lurk Behind Lower Blood Pressure? 120
Example 4.2 The Fewer the Pages, the More Valuable the Book? 121
Case Study 4.1 Lead Exposure and Bad Teeth 122
Case Study 4.2 Kids and Weight Lifting 124
Example 4.3 Randomly Assigning Children to Weight-Lifting Groups 127
Case Study 4.3 Quitting Smoking with Nicotine Patches 129
*Examples marked by an asterisk are revisited for further discussion later in the chapter.

Case Study 4.4 Baldness and Heart Attacks 133
Example 4.4 Will Preventing Artery Clog Prevent Memory Loss? 137
Example 4.5 Dull Rats 139
Example 4.6 Real Smokers with a Desire to Quit 140
Example 4.7 Do Left-Handers Die Young? 140

Chapter 5
Example 5.1 Height and Handspan 152
Example 5.2 Driver Age and the Maximum Legibility Distance of Highway Signs 153
Example 5.3 The Development of Musical Preferences 154
Example 5.4 Heights and Foot Lengths of College Women 156
Example 5.5 Describing Height and Handspan with a Regression Line 158
Example 5.6 Regression for Driver Age and the Maximum Legibility Distance of Highway Signs 161
Example 5.7 Prediction Errors for the Highway Sign Data 162
Example 5.8 Calculating the Sum of Squared Errors 164
Example 5.9 The Correlation Between Handspan and Height 166
Example 5.10 The Correlation Between Age and Sign Legibility Distance 167
Example 5.11 Left and Right Handspans 167

Example 5.12 Verbal SAT and GPA 168
Example 5.13 Age and Hours of Television Viewing per Day 168
Example 5.14 Hours of Sleep and Hours of Study 169
Example 5.15 Height and Foot Length of College Women 172
Example 5.16 Earthquakes in the Continental United States 172
Example 5.17 Does It Make Sense? Height and Lead Feet 173
Example 5.18 Does It Make Sense? U.S. Population Predictions 174
Case Study 5.1 A Weighty Issue 179

Chapter 6
Example 6.1 Smoking and the Risk of Divorce 195
Example 6.2 Tattoos and Ear Pierces 196
Example 6.3 Gender and Reasons for Taking Care of Your Body 197
Example 6.4 Smoking and Relative Risk of Divorce 198
Example 6.5 Percent Increase in the Risk of Divorce for Smokers 199
Example 6.6 The Risk of a Shark Attack 201
Example 6.7 Disaster in the Skies? Case Study 1.2 Revisited 202
Example 6.8 Dietary Fat and Breast Cancer 202
Case Study 6.1 Is Smoking More Dangerous for Women? 203
Example 6.9 Educational Status and Driving after Substance Use 204
Example 6.10 Blood Pressure and Oral Contraceptive Use 205
Example 6.11 A Table of Expected Counts 209
Example 6.12 Does Order Influence Who Wins an Election? 211
Example 6.13 Breast Cancer Risk Stops Hormone Replacement Therapy Study 212
Example 6.14 Aspirin and Heart Attacks 214
Case Study 6.2 Drinking, Driving, and the Supreme Court 216

Chapter 7
Case Study 7.1 A Hypothetical Story: Alicia Has a Bad Day 230
Example 7.1 Probability of Male Versus Female Births 232

Example 7.2 A Simple Lottery 233
Example 7.3 The Probability That Alicia Has to Answer a Question 233
Example 7.4 The Probability of Lost Luggage 234
Example 7.5 Nightlights and Myopia Revisited 235
Example 7.6 Days per Week of Drinking Alcohol 238
Example 7.7 Probabilities for Some Lottery Events 239
Example 7.8 The Probability of Not Winning the Lottery 239
Example 7.9 Mutually Exclusive Events for Lottery Numbers 240
Example 7.10 Winning a Free Lunch 241
Example 7.11 The Probability That Alicia Has to Answer a Question 241
Example 7.12 Probability That a Teenager Gambles Depends upon Gender 242
Example 7.13 Probability a Stranger Does Not Share Your Birth Date 243
Example 7.14 Roommate Compatibility 244
Example 7.15 Probability of Either Two Boys or Two Girls in Two Births 245


Example 7.16 Probability That a Randomly Selected Ninth-Grader Is a Male and a Weekly
Gambler 246
Example 7.17 Probability That Two Strangers Both Share Your Birth Month 246
Example 7.18 Probability Alicia Is Picked for the First Question Given That She’s Picked to Answer
a Question 247
Example 7.19 The Probability of Guilt and Innocence Given a DNA Match 248
Example 7.20 Winning the Lottery 251
Example 7.21 Prizes in Cereal Boxes 252
Example 7.22 Family Composition 253
Example 7.23 Optimism for Alicia—She Is Probably Healthy 254
Example 7.24 Two-Way Table for Teens and Gambling 255
Example 7.25 Alicia’s Possible Fates 256
Example 7.26 The Probability That Alicia Has a Positive Test 257
Example 7.27 Tree Diagram for Teens and Gambling 257

Example 7.28 Getting All the Prizes 259
Example 7.29 Finding Gifted ESP Participants 260
Example 7.30 Two George D. Brysons 264
Example 7.31 Identical Cars and Matching Keys 264
Example 7.33 Winning the Lottery Twice 265
Example 7.34 Unusual Hands in Card Games 266
Case Study 7.2 Doin’ the iPod ® Shuffle 268

Chapter 8
Example 8.1 Random Variables at an Outdoor Graduation or Wedding 280
Example 8.2 It’s Possible to Toss Forever 281
Example 8.3 Probability an Event Occurs Three Times in Three Tries 281
Example 8.4 Waiting on Standby 282
Example 8.5 Probability Distribution Function for Number of Courses 283
Example 8.6 Probability Distribution Function for Number of Girls 284
Example 8.7 Graph of pdf for Number of Girls 285
Example 8.8 Cumulative Distribution for the Number of Girls 286
Example 8.9 A Mixture of Children 287
Example 8.10 Probabilities for Sum of Two Dice 287
Example 8.11 Gambling Losses 289
Example 8.12 California Decco Lottery Game 290
Example 8.13 Stability or Excitement—Same Mean, Different Standard Deviations 291
Example 8.14 Mean Hours of Study for the Class Yesterday 293
Example 8.15 Probability of Two Wins in Three Plays 296
Example 8.16 Excel Calculations for Number of Girls in Ten Births 297
Example 8.17 Guessing Your Way to a Passing Score 297
Example 8.18 Is There Extraterrestrial Life? 299
Case Study 8.1 Does Caffeine Enhance the Taste of Cola? 299
Example 8.19 Time Spent Waiting for the Bus 301
Example 8.20 Probability That the Waiting Time is Between 5 and 7 Minutes 301

Example 8.21 College Women’s Heights 303
Example 8.22 The z-Score for a Height of 62 Inches 304
Example 8.23 Probability That Height is Less Than 62 Inches 306
Example 8.24 Probability That Z Is Greater Than 1.31 307
Example 8.25 Probability That Height Is Greater Than 68 Inches 308
Example 8.26 Probability That Z Is Between ؊2.59 and 1.31 308
Example 8.27 Probability That a Vehicle Speed Is Between 60 and 70 mph 309
Example 8.28 The 75th Percentile of Systolic Blood Pressures 310
Example 8.29 The Number of Heads in 30 Flips of a Coin 312
Example 8.30 Political Woes 313
Example 8.31 Guessing and Passing a True-False Test 313
Example 8.32 Will Meg Miss Her Flight? 317
Example 8.33 Can Alison Ever Win? 317
Example 8.34 Donations Add Up 318
Example 8.35 Strategies for Studying When You Are Out of Time 319

Chapter 9
Example 9.1
Example 9.2
Example 9.3
Example 9.4

The “Freshman 15” 334
Opinions About Genetically Modified Food 339
Probability of Quitting with a Nicotine Patch 339
How Much More Likely Are Smokers to Quit with a Nicotine Patch? 340

Example 9.5 Age of First Intercourse for Females 341
Example 9.6 Which Hand Is Bigger? 341
Example 9.7 Do Girls and Boys Have First Intercourse at the Same Age on Average? 342

Example 9.8 Mean Hours of Sleep for College Students 345
Example 9.9 Possible Sample Proportions Favoring a Candidate 351
Example 9.10 Caffeinated or Not? 352
Example 9.11 Men, Women, and the Death Penalty 356
Example 9.12 Hypothetical Mean Weight Loss 360
Example 9.13 Suppose There Is No “Freshman 15” 364
Example 9.14 Who Are the Speed Demons? 367
Example 9.15 Unpopular TV Shows 369
Example 9.16 Standardized Mean Weights 371
Example 9.17 The Long Run for the Decco Lottery Game 374
Example 9.18 California Decco Losses 375
Example 9.19 Winning the Lottery by Betting on Birthdays 377
Example 9.20 Constructing a Simple Sampling Distribution for the Mean Movie Rating 378
Case Study 9.1 Do Americans Really Vote When They Say They Do? 382

Chapter 10
Example 10.1 Teens and Interracial Dating: Case Study 1.3 Revisited 405
Example 10.2 The Pollen Count Must Be High Today 409
Example 10.3 Is There Intelligent Life on Other Planets? 412
Example 10.4 50% Confidence Interval for Proportion Believing That Intelligent Life Exists
Elsewhere 413
Example 10.5 College Men and Ear Pierces 415
Example 10.6 Would You Return a Lost Wallet? 415
Example 10.7 Winning the Lottery and Quitting Work 421
Example 10.8 The Gallup Poll Margin of Error for n ‫ ؍‬1000 422
Example 10.9 Allergies and Really Bad Allergies 423
Example 10.10 Snoring and Heart Attacks 425
Example 10.11 Do You Always Buckle Up When Driving? 426
Example 10.12 Which Drink Tastes Better? 429
Case Study 10.1 Extrasensory Perception Works with Movies 429

Case Study 10.2 Nicotine Patches versus Zyban ® 430
Case Study 10.3 What a Great Personality 431

Chapter 11
Example 11.1 Pet Ownership and Stress 446
Example 11.2 Mean Hours per Day That Penn State Students Watch TV 448
Example 11.3 Do Men Lose More Weight by Diet or by Exercise? 449
Example 11.4 Finding the t* Values for 24 Degrees of Freedom and 95% or 99% Confidence
Intervals 451
Example 11.5 Are Your Sleeves Too Short? The Mean Forearm Length of Men 454
Example 11.6 How Much TV Do Penn State Students Watch? 455
Example 11.7 What Type of Students Sleep More? 457
Example 11.8 Approximate 95% Confidence Interval for TV Time 460
Example 11.9 Screen Time—Computer Versus TV 463
Example 11.10 Meditation and Anxiety 465
Example 11.11 The Effect of a Stare on Driving Behavior 468
Example 11.12 Parental Alcohol Problems and Child Hangover Symptoms 471
Example 11.13 Confidence Interval for Difference in Mean Weight Losses by Diet or Exercise 472
Example 11.14 Pooled t-Interval for Difference Between Mean Female and Male Sleep Times 474
Example 11.15 Sleep Time with and Without the Equal Variance Assumption 476
Case Study 11.1 Confidence Interval for Relative Risk: Case Study 4.4 Revisited 478

Chapter 12
Example 12.1
Example 12.2
Example 12.3
Example 12.4
Example 12.5
Example 12.6
Example 12.7

Example 12.8

Are Side Effects Experienced by Fewer Than 20% of Patients? 497
Does a Majority Favor the Proposed Blood Alcohol Limit? 498
Psychic Powers 499
Stop the Pain before It Starts 500
A Jury Trial 504
Errors in the Courtroom 504
Errors in Medical Tests 505
Calcium and the Relief of Premenstrual Symptoms 506


Example 12.9 Medical Tests Revisited 507
*Example 12.10 The Importance of Order in Voting 512
*Example 12.11 Do Fewer Than 20% Experience Medication Side Effects? 516
Example 12.12 A Test for Extrasensory Perception 519
Example 12.13 A Two-Sided Test: Are Left and Right Foot Lengths Equal? 520
Example 12.14 Making Sure Students Aren’t Guessing 521
Example 12.15 What Do Men Care About in a Date? 522
Example 12.16 Power and Sample Size for a Survey of Students 524
*Example 12.17 The Prevention of Ear Infections 528
Example 12.18 How the Same Sample Proportion Can Produce Different Conclusions 533
Example 12.19 Birth Month and Height 536

Chapter 13
*Example 13.1 Normal Human Body Temperature 553
Example 13.2 The Effect of Alcohol on Useful Consciousness 562
*Example 13.3 The Effect of a Stare on Driving Behavior 565
Example 13.4 A Two-Tailed Test of Television Watching for Men and Women 568
Example 13.5 Misleading Pooled t-Test for Television Watching for Men and Women 572

Example 13.6 Legitimate Pooled t-Test for Comparing Male and Female Sleep Time 573
Example 13.7 Mean Daily Television Hours of Men and Women 575
Example 13.8 Ear Infections and Xylitol 576
Example 13.9 Kids and Weight Lifting 579
Example 13.10 Loss of Cognitive Functioning 580
Example 13.11 Could Aliens Tell That Women Are Shorter? 582
Example 13.12 Normal Body Temperature 583
Example 13.13 The Hypothesis-Testing Paradox 583
Example 13.14 Planning a Weight-Loss Study 584

Chapter 14
Example 14.1 Residuals in the Handspan and Height Regression 602
Example 14.2 Mean and Deviation for Height and Handspan Regression 604
Example 14.3 Relationship Between Height and Weight for College Men 606
Example 14.4 R 2 for Heights and Weights of College Men 608
Example 14.5 Driver Age and Highway Sign-Reading Distance 608
Example 14.6 Hypothesis Test for Driver Age and Sign-Reading Distance 610
Example 14.7 95% Confidence Interval for Slope Between Age and Sign-Reading Distance 611
Example 14.8 Estimating Mean Weight of College Men at Various Heights 617
Example 14.9 Checking the Conditions for the Weight and Height Problem 620
Case Study 14.1 A Contested Election 623

Chapter 15
Example 15.1 Ear Infections and Xylitol Sweetener 636
Example 15.2 With Whom Do You Find It Easiest to Make Friends? 637
Example 15.3 Calculation of Expected Counts and Chi-Square for the Xylitol and Ear Infection
Data 639
Example 15.4 p-Value Area for the Xylitol Example 641
Example 15.5 Using Table A.5 for the Xylitol and Ear Infection Problem 642
Example 15.6 A Moderate p-Value 643

Example 15.7 A Tiny p-Value 643
Example 15.8 Making Friends 644
Example 15.9 Gender, Drinking, and Driving 647
Example 15.10 Age and Tension Headaches 648
Example 15.11 Sheep, Goats, and ESP 649
Example 15.12 Butterfly Ballots 650
Example 15.13 The Pennsylvania Daily Number 654
Case Study 15.1 Do You Mind If I Eat the Blue Ones? 657

Chapter 16
Example 16.1
Example 16.2
Example 16.3
Example 16.4

Classroom Seat Location and Grade Point Average 670
Application of Notation to the GPA and Classroom Seat Sample 672
Assessing the Necessary Conditions for the GPA and Seat Location Data 673
Occupational Choice and Testosterone Level 674

Example 16.5 The p-Value for the Testosterone and Occupational Choice Example 676
Example 16.6 Pairwise Comparisons of GPAs Based on Seat Locations 677
Example 16.7 Comparison of Weight-Loss Programs 680
Example 16.8 Analysis of Variation Among Weight Losses 681
Example 16.9 Top Speeds of Supercars 683
Example 16.10 95% Confidence Intervals for Mean Car Speeds 684
Example 16.11 Drinks per Week and Seat Location 685
Example 16.12 Kruskal–Wallis Test for Alcoholic Beverages per Week by Seat Location 687
Example 16.13 Mood’s Median Test for the Alcoholic Beverages and Seat Location Example 688
Example 16.14 Happy Faces and Restaurant Tips 690

Example 16.15 You’ve Got to Have Heart 691
Example 16.16 Two-Way Analysis of Variance for Happy Face Example 692

Chapter 17
Example 17.1
Example 17.2
Example 17.3
Example 17.4
Example 17.5
Example 17.6
Example 17.7
Example 17.8
Example 17.9

Playing the Lottery 710
Surgery or Uncertainty? 710
Fish Oil and Psychiatric Disorders 711
Go, Granny, Go or Stop, Granny, Stop? 713
When Smokers Butt Out, Does Society Benefit? 714
Is It Wining or Dining That Helps French Hearts? 716
Give Her the Car Keys 717
Lifestyle Statistics from the Census Bureau 718
In Whom Do We Trust? 719

Supplemental Topic 1
*Example S1.1
*Example S1.2
Example S1.3
Example S1.4
Example S1.5

Example S1.6
Example S1.7

Random Security Screening S1-3
Betting Birthdays for the Lottery S1-3
Customers Entering a Small Shop S1-8
Earthquakes in the Coming Year S1-10
Emergency Calls to a Small Town Police Department S1-10
Are There Illegal Drugs in the Next 5000 Cars? S1-11
Calling On the Back of the Class S1-13

Supplemental Topic 2
Example S2.1
Example S2.2
*Example S2.3
Example S2.4
Example S2.5
Example S2.6

Normal Human Body Temperature S2-5
Heights of Male Students and Their Fathers S2-6
Estimating the Size of Canada’s Population S2-9
Calculating T ؉ for a Sample of Systolic Blood Pressures S2-13
Difference Between Student Height and Mother’s Height for College Women S2-14
Comparing the Quality of Wine Produced in Three Different Regions S2-17

Supplemental Topic 3
*Example S3.1 Predicting Average August Temperature S3-3
*Example S3.2 Blood Pressure of Peruvian Indians S3-4

Supplemental Topic 4
*Example S4.1
*Example S4.2
Example S4.3
Example S4.4
Example S4.5

Sleep Hours Based on Gender and Seat Location S4-2
Pulse Rates, Gender, and Smoking S4-6
Nature Versus Nurture in IQ Scores S4-14
Happy Faces and Restaurant Tips Revisited S4-16
Does Smoking Lead to More Errors? S4-18

Supplemental Topic 5
Example S5.1 Stanley Milgram’s “Obedience and Individual Responsibility” Experiment S5-3
Example S5.2 Janet’s (Hypothetical) Dissertation Research S5-12
Example S5.3 Jake’s (Hypothetical) Fishing Expedition S5-14
Example S5.4 The Debate Over Passive Smoking S5-15
Example S5.5 Helpful and Harmless Outcomes from Hormone Replacement Therapy S5-18
Case Study S5.1 Science Fair Project or Fair Science Project? S5-19


Mind on Statistics
Third Edit ion

Jessica M. Utts
University of California, Davis

Robert F. Heckard
Pennsylvania State University

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Mind on Statistics, Third Edition
Jessica M. Utts and Robert F. Heckard
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To Bill Harkness— energetic, generous, and innovative
educator, guide, and friend—who launched our careers
in statistics and continues to share his vision.


Brief Contents

1
2
3
4

Statistics Success Stories and Cautionary Tales

5
6
7
8
9

Relationships Between Quantitative Variables

10
11
12
13
14

15
16
17
iv

Turning Data Into Information

1

12

Sampling: Surveys and How to Ask Questions

70

Gathering Useful Data for Examining
Relationships 116
Relationships Between Categorical Variables
Probability

150
192

228

Random Variables

278

Understanding Sampling Distributions: Statistics

as Random Variables 330
Estimating Proportions with Confidence
Estimating Means with Confidence

442

Testing Hypotheses About Proportions
Testing Hypotheses About Means
Inference About Simple Regression

400
494

550
598

More About Inference for Categorical Variables
Analysis of Variance

668

Turning Information Into Wisdom

704

634


Contents
1

Statistics Success Stories
and Cautionary Tales 1
1.1 What Is Statistics? 1
1.2 Seven Statistical Stories with Morals 2
1.3 The Common Elements in the Seven Stories

7

Key Terms 8
Exercises 9

2

Turning Data Into Information
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8

12

Raw Data 13
Types of Variables 15
Summarizing One or Two Categorical Variables 19
Exploring Features of Quantitative Data with Pictures

Numerical Summaries of Quantitative Variables 36
How to Handle Outliers 47
Features of Bell-Shaped Distributions 49
Skillbuilder Applet: The Empirical Rule in Action 56

24

Key Terms 57
Exercises 58

3

Sampling: Surveys and How
to Ask Questions 70
3.1
3.2
3.3
3.4
3.5

Collecting and Using Sample Data Wisely 71
Margin of Error, Confidence Intervals, and Sample Size
Choosing a Simple Random Sample 80
Other Sampling Methods 83
Difficulties and Disasters in Sampling 89

75

v

vi

Contents

3.6 How to Ask Survey Questions 95
3.7 Skillbuilder Applet: Random Sampling in Action

103

Key Terms 106
Exercises 106

4

Gathering Useful Data
for Examining Relationships
4.1
4.2
4.3
4.4

116

Speaking the Language of Research Studies 117
Designing a Good Experiment 124
Designing a Good Observational Study 133
Difficulties and Disasters in Experiments
and Observational Studies 136
Key Terms 141

Exercises 142

5

Relationships Between
Quantitative Variables 150
5.1
5.2
5.3
5.4
5.5
5.6

Looking for Patterns with Scatterplots 152
Describing Linear Patterns with a Regression Line 157
Measuring Strength and Direction with Correlation 165
Regression and Correlation Difficulties and Disasters 171
Correlation Does Not Prove Causation 176
Skillbuilder Applet: Exploring Correlation 178
Key Terms 181
Exercises 181

6

Relationships Between
Categorical Variables 192
6.1
6.2
6.3
6.4

Displaying Relationships Between Categorical Variables 193
Risk, Relative Risks, and Misleading Statistics About Risk 198
The Effect of a Third Variable and Simpson’s Paradox 204
Assessing the Statistical Significance of a 2 ϫ 2 Table 206
Key Terms 216
Exercises 217

7

Probability

228

7.1 Random Circumstances 229
7.2 Interpretations of Probability 231


Contents

7.3
7.4
7.5
7.6
7.7

vii

Probability Definitions and Relationships 238
Basic Rules for Finding Probabilities 243

Strategies for Finding Complicated Probabilities 251
Using Simulation to Estimate Probabilities 259
Flawed Intuitive Judgments About Probability 261
Key Terms 269
Exercises 269

8

Random Variables
8.1
8.2
8.3
8.4
8.5
8.6
8.7
8.8

278

What Is a Random Variable? 279
Discrete Random Variables 283
Expectations for Random Variables 288
Binomial Random Variables 294
Continuous Random Variables 300
Normal Random Variables 302
Approximating Binomial Distribution Probabilities
Sums, Differences, and Combinations
of Random Variables 314

311

Key Terms 320
Exercises 321

9

Understanding Sampling Distributions:
Statistics as Random Variables 330
9.1
9.2
9.3
9.4
9.5
9.6
9.7
9.8
9.9
9.10
9.11

Parameters, Statistics, and Statistical Inference 331
From Curiosity to Questions About Parameters 334
SD Module 0: An Overview of Sampling Distributions 344
SD Module 1: Sampling Distribution for One Sample
Proportion 348
SD Module 2: Sampling Distribution for the Difference
in Two Sample Proportions 354
SD Module 3: Sampling Distribution for One
Sample Mean 357

SD Module 4: Sampling Distribution for the Sample Mean
of Paired Differences 362
SD Module 5: Sampling Distribution for the Difference
in Two Sample Means 365
Preparing for Statistical Inference:
Standardized Statistics 368
Generalizations Beyond the Big Five 373
Skillbuilder Applet: Finding the Pattern in Sample Means 379


viii

Contents

Key Terms 383
Exercises 384

10

Estimating Proportions
with Confidence 400
10.1
10.2
10.3

Introduction 401
CI Module 0: An Overview of Confidence Intervals 403
CI Module 1: Confidence Interval for a Population
Proportion 410
10.4 CI Module 2: Confidence Intervals for the Difference

in Two Population Proportions 423
10.5 Using Confidence Intervals to Guide Decisions 428
Key Terms 432
Exercises 433

11

Estimating Means with Confidence
11.1
11.2
11.3
11.4
11.5
11.6

442

Introduction to Confidence Intervals for Means 444
CI Module 3: Confidence Interval for One
Population Mean 452
CI Module 4: Confidence Interval for the Population Mean
of Paired Differences 461
CI Module 5: Confidence Interval for the Difference in Two
Population Means 466
Understanding Any Confidence Interval 478
Skillbuilder Applet: The Confidence Level in Action 480
Key Terms 483
Exercises 484

12

Testing Hypotheses
About Proportions 494
12.1
12.2
12.3

Introduction 495
HT Module 0: An Overview of Hypothesis Testing 496
HT Module 1: Testing Hypotheses About a Population
Proportion 511
12.4 HT Module 2: Testing Hypotheses About the Difference
in Two Population Proportions 527
12.5 Sample Size, Statistical Significance, and Practical
Importance 533
Key Terms 538
Exercises 538


Contents

13

Testing Hypotheses About Means
13.1
13.2
13.3
13.4
13.5
13.6

13.7
13.8

ix

550

Introduction to Hypothesis Tests for Means 551
HT Module 3: Testing Hypotheses about One
Population Mean 553
HT Module 4: Testing Hypotheses about the Population
Mean of Paired Differences 561
HT Module 5: Testing Hypotheses about the Difference
in Two Population Means 565
The Relationship Between Significance Tests
and Confidence Intervals 574
Choosing an Appropriate Inference Procedure 576
Effect Size 580
Evaluating Significance in Research Reports 585
Key Terms 587
Exercises 588

14

Inference About Simple Regression
14.1
14.2
14.3
14.4
14.5

Sample and Population Regression Models 600
Estimating the Standard Deviation for Regression 605
Inference About the Slope of a Linear Regression 609
Predicting y and Estimating Mean y at a Specific x 613
Checking Conditions for Using Regression Models
for Inference 619
Key Terms 625
Exercises 626

15

More About Inference
for Categorical Variables
15.1
15.2
15.3

634

The Chi-Square Test for Two-Way Tables 635
Analyzing 2 ϫ 2 Tables 646
Testing Hypotheses About One Categorical Variable:
Goodness-of-Fit 652
Key Terms 658
Exercises 658

16

Analysis of Variance

16.1
16.2

598

668

Comparing Means with an ANOVA F-Test 669
Details of One-Way Analysis of Variance 679


x

Contents

16.3
16.4

Other Methods for Comparing Populations
Two-Way Analysis of Variance 689

685

Key Terms 693
Exercises 693

17

Turning Information Into Wisdom
17.1

17.2
17.3
17.4
17.5
17.6
17.7

Beyond the Data 705
Transforming Uncertainty Into Wisdom
Making Personal Decisions 709
Control of Societal Risks 713
Understanding Our World 715
Getting to Know You 718
Words to the Wise 719
Exercises

709

721

Appendix of Tables 725
References 733
Answers to Selected Exercises
Credits 751
Index 753

738

The Supplemental Topics are available on the Student’s Suite CD,
or print copies may be custom published.

SUPPLEMENTAL TOPIC

1

Additional Discrete Random
Variables S1-1
S1.1
S1.2
S1.3

Hypergeometric Distribution S1-2
Poisson Distribution S1-7
Multinomial Distribution S1-11
Key Terms S1-13
Exercises S1-14

SUPPLEMENTAL TOPIC

2

Nonparametric Tests
of Hypotheses S2-1
S2.1
S2.2

The Sign Test S2-3
The Two-Sample Rank-Sum Test

S2-7

704


xi

Contents

S2.3
S2.4

The Wilcoxon Signed-Rank Test
The Kruskal–Wallis Test S2-16

S2-12

Key Terms S2-19
Exercises S2-19

SUPPLEMENTAL TOPIC

3

Multiple Regression
S3.1
S3.2
S3.3

S3-1

The Multiple Linear Regression Model S3-3
Inference About Multiple Regression Models S3-9
Checking Conditions for Multiple Linear Regression

S3-14

Key Terms S3-16
Exercises S3-16

SUPPLEMENTAL TOPIC

4

Two-Way Analysis of Variance
S4.1
S4.2

S4-1

Assumptions and Models for Two-Way ANOVA S4-2
Testing for Main Effects and Interactions S4-9
Key Terms S4-20
Exercises S4-21

SUPPLEMENTAL TOPIC

5

Ethics
S5.1

S5.2
S5.3
S5.4

S5-1

Ethical Treatment of Human and Animal Participants
Assurance of Data Quality S5-9
Appropriate Statistical Analysis S5-14
Fair Reporting of Results S5-16
Key Terms S5-20
Exercises S5-21

S5-2


This page intentionally left blank


Preface

A Challenge
Before you continue, think about how you would answer the question in the
first bullet, and read the statement in the second bullet. We will return to them
a little later in this Preface.
●

What do you really know is true, and how do you know it?

●

The diameter of the moon is about 2160 miles.

What Is Statistics and Who Should Care?
Because people are curious about many things, chances are that your interests
include topics to which statistics has made a useful contribution. As written in
Chapter 17, “information developed through the use of statistics has enhanced
our understanding of how life works, helped us learn about each other, allowed
control over some societal issues, and helped individuals make informed decisions. There is almost no area of knowledge that has not been advanced by statistical studies.”
Statistical methods have contributed to our understanding of health, psychology, ecology, politics, music, lifestyle choices, business, commerce, and
dozens of other topics. A quick look through this book, especially Chapters 1
and 17, should convince you of this. Watch for the influences of statistics in your
daily life as you learn this material.

How Is this Book Different?
Two Basic Premises of Learning
We wrote this book because we were tired of being told that what statisticians
do is boring and difficult. We think statistics is useful and not difficult to learn,
and yet the majority of college graduates we’ve met seemed to have had a negative experience taking a statistics class in college. We hope this book will help to
overcome these misguided stereotypes.
Let’s return to the two bullets at the beginning of this Preface. Without looking, do you remember the diameter of the moon? Unless you already had a
xiii


xiv

Preface

pretty good idea, or have an excellent memory for numbers, you probably don’t
remember. One premise of this book is that new material is much easier to

learn and remember if it is related to something interesting or previously
known. The diameter of the moon is about the same as the air distance between
Atlanta and Los Angeles, San Francisco and Chicago, London and Cairo, or
Moscow and Madrid. Picture the moon sitting between any of those pairs of
cities, and you are not likely to forget the size of the moon again. Throughout
this book, new material is presented in the context of interesting and useful
examples. The first and last chapters (1 and 17) are exclusively devoted to examples and case studies, which illustrate the wisdom that can be generated
through statistical studies.
Now answer the question asked in the first bullet: What do you really know
is true and how do you know it? If you are like most people, you know because
it’s something you have experienced or verified for yourself. It is not likely to be
something you were told or heard in a lecture. The second premise of this book
is that new material is easier to learn if you actively ask questions and answer
them for yourself. Mind on Statistics is designed to help you learn statistical
ideas by actively thinking about them. Throughout most of the chapters there
are boxes entitled Thought Questions. Thinking about the questions in those
boxes will help you to discover and verify important ideas for yourself. We encourage you to think and question, rather than simply read and listen.

New to this Edition
The biggest changes have been made in Chapters 9 to 13, containing the core
material on sampling distributions and statistical inference. The new organization presents the material in a modular, more flexible format. There are six
modules for each of the topics of sampling distributions, confidence intervals,
and hypothesis testing. The first module presents an introduction and the remaining five modules each deal with a specific parameter, such as one mean,
one proportion, or the difference in two means. Chapter 9 covers sampling distributions, Chapters 10 and 11 cover confidence intervals, and Chapters 12 and
13 cover hypothesis testing.
In response to reviewer feedback, we made these changes for two reasons: pedagogy and practicality. The new structure emphasizes the similarity
among the inference procedures for the five parameters discussed. It allows
instructors to illustrate that each procedure covered is a specific instance of the
same process. We recognize that instructors have different preferences for the
order in which to cover inference topics. For instance, some prefer to first cover

all topics about proportions and then cover all topics about means. Others prefer to first cover everything about confidence intervals and then cover everything about hypothesis testing. With the new modular format, instructors can
cover these topics in the order they prefer.
To aid in the navigation through these modular chapters, we have added
color-coded, labeled tabs that correspond to the introductory and parameter
modules. The table below, also found in Chapter 9, lays out the color-coding
system as well as the flexibility of these new chapters. In addition, the table is a
useful course planning tool.


Preface: Tools for Conceptual Understanding

xv

Organization of Chapters 9 to 13

Parameter
0. Introductory

1. Population
Proportion (p)
2. Difference in two
population proportions
(p1 ؊ p2)
3. Population mean (M)
4. Population mean of
paired differences (Md )
5. Difference in two
population means
(M1 ؊ M2)

Chapter 9:
Sampling
Distributions (SD)

Chapter 10:
Confidence
Intervals (CI)

Chapter 11:
Confidence
Intervals (CI)

SD Module 0
Overview of sampling
distributions
SD Module 1
SD for one sample
proportion
SD Module 2
SD for difference in two
sample proportions
SD Module 3
SD for one sample mean
SD Module 4
SD for sample mean of
paired differences
SD Module 5
SD for difference in two
sample means

CI Module 0
Overview of confidence
intervals
CI Module 1
CI for one population
proportion
CI Module 2
CI for difference in two
population proportions

Chapter 12:
Hypothesis
Tests (HT)

Chapter 13:
Hypothesis
Tests (HT)

HT Module 0
Overview of hypothesis
testing
HT Module 1
HT for one population
proportion
HT Module 2
HT for difference in two
population proportions
CI Module 3
CI for one population mean
CI Module 4

CI for population mean of
paired differences
CI Module 5
CI for difference in two
population means

HT Module 3
HT for one population mean
HT Module 4
HT for population mean of paired
differences
HT Module 5
HT for difference in two
population means

In response to feedback from users, some other chapters have been expanded and reorganized. Most notably, Chapters 3 and 4 have been essentially
reversed, so that random samples and surveys are presented before the more
complicated studies based on randomized experiments and observational
studies.
Furthermore, to add to the flexibility of topic coverage, Supplemental Topics 1 to 5 on discrete random variables, nonparametric tests, multiple regression, two-way ANOVA, and ethics are now available for use in both print and
electronic formats. Instructors, please contact your sales representative to find
out how these chapters can be custom published for your course.

Student Resources:
Tools for Expanded Learning
There are a number of tools provided in this book and beyond to enhance your
learning of statistics.

Tools for Conceptual Understanding
Updated! Thought

Q u e s t i o n boxes, previously
called “Turn on Your Mind,” appear
throughout each chapter to encourage active thinking and questioning
about statistical ideas. Hints are
provided at the bottom of the page
to help you develop this skill.

t hought ques t i on 3.5 You have now learned that survey results have to be interpreted in the
context of who responded and to what questions they responded. When you read
the results of a survey, for which of these two areas do you think it would be easier for
you to recognize and assess possible biases? Why?*

*H I N T :

What information would you need in each case, and what information is more likely to
be included in a description of the survey?


xvi

Preface: Tools for Conceptual Understanding

Updated! Skillbuilder
A p p l e t sections, previously
called “Turn on Your Computer,”
provide opportunities for in-class or
independent hands-on exploration
of key statistical concepts. The
applets that accompany this feature can be found on the Student’s
Suite CD or at http://1pass

.thomson.com.

skillbuilder applet

9.11 Finding the Pattern in Sample Means
The main idea for any sampling distribution is that it gives the pattern for how
the potential value of a statistic may vary from sample to sample. The Rule for
Sample Means tells us that in two common situations, a normal curve approximates the sampling distribution of the sample mean. The SampleMeans applet
lets us see the pattern that emerges when we look at the means of many different random samples from the same population. Figure 9.13 illustrates the

To explore this applet and work
through this activity, go to Chapter 9
at and
click on Skillbuilder Applet, or view
the applet on your CD.

Figure 9.13 ❚ The SampleMeans applet starting point

U p d a t e d ! Te c h n i c a l
No t e s boxes, previously
called “Tech Notes,” provide
additional technical discussion
of key concepts.

techni cal note

The Number of Units Per Block
Some statisticians argue that the number of experimental units in a block
should equal the number of treatments so that each treatment is assigned
only once in each block. That allows as many sources of known variability

as possible to be controlled. For the example of the effect of caffeine on
swimming speed, there are two treatments, so blocks of size 2 (matched
pairs) would be created. This could be accomplished by matching people on
known variables such as sex, initial swim speed, usual caffeine consumption, age, and so on.

Investigating Real-Life Questions
U p d a t e d ! Relevant E x a m p l e s form the basis for discussion in each chapter and walk
you through real-life uses of statistical concepts.

Example 9.1

The “Freshman 15” Do college students really gain weight during their freshman year? The lore is that they do, and this phenomenon has been called “the
freshman 15” because of speculation that students typically gain as much as 15
pounds during the first year of college. How can we turn our curiosity about the
freshman 15 into a question about a parameter? There are several ways in which
we could investigate whether or not students gain weight during the first year.
We might want to know what proportion of freshmen gain weight. A related
question would be whether most students gain weight. Or we might want to
know what the average weight gain is across all first-year students. We might
want to know whether women gain more weight than men or vice versa. Here
are two ideas for satisfying our curiosity, along with the standard notation that
we use for the relevant parameters:
●

Parameter ϭ p ϭ proportion of the population of first-year college students who weigh more at the end of the year than they did at the beginning of the year.

●

Parameter ϭ md ϭ the mean (average) weight gain during the first year for
the population of college students. The subscript d indicates that the raw

Preface: Investigating Real-Life Questions
Updated! Case Studies
apply statistical ideas to intriguing
news stories. As the Case Studies
are developed, they model the
statistical reasoning process.

case s tudy 7.2

Doin’ the iPod Random Shuffle

The ability to play a collection of songs in a random order is a popular feature of portable digital music players. As an example, an Apple iPod Shuffle
player with 512 megabytes of memory can store about 120 songs. Players
with larger memory can store and randomly order thousands of songs. When
the shuffle function is used, the stored songs are played in a random order.
We mention the iPod because there has been much grumbling, particularly on the Internet, that its shuffle might not be random. Some users
complain that a song might be played within the first hour in two or three
consecutive random shuffles. A similar complaint is that there are clusters
of songs by the same group or musician within the first hour or so of
play. Newsweek magazine’s technology writer, Steven Levy (31 January
2005), wrote, “From the day I first loaded up my first Pod, it was as if the
little devil liked to play favorites. It had a particular fondness for Steely Dan,
whose songs always seemed to pop up two or three times in the first hour
of play. Other songs seemed to be exiled to a forgotten corner” (p. 10).
Conspiracy theorists even accuse Apple of playing favorites, giving certain
musicians a better chance to have their songs played early in the shuffle.

case s tudy 10.3

●

61.1% of 131 women answered “yes.”

●

42.6% of 61 men answered “yes.”

For men, the approximate 95% confidence interval is 30.2% to 55%.

Example 5.3

Read the original source on your CD.

Event A ϭ first song is anything.
Event B ϭ second song is from different album than first.
C ϭ third song is from different album than first two picked.
D ϭ fourth song is from different album than first three picked.

The probability that all four are from different albums is
P (A and B and C and D) ϭ

120
108
96
84
ϫ
ϫ
ϫ

ϭ .53
120
119
118
117

Thus, the probability that all four songs are not from different albums, meaning that at least two songs are from the same album, is
P (at least two of first four are from the same album) ϭ 1 Ϫ .53
ϭ .47

make any conclusions about whether there is a difference in the population
proportions. Instead, we can find a confidence interval for the difference in
the proportions of men and women who would answer yes to the question.
Men
Women

There clearly is a difference between the men and the women in these
samples. Can this difference be generalized to the populations represented
by the samples? This question brings up another question: What are the populations that we are comparing? These men and women weren’t randomly
picked from any particular population, but for this example, we’ll assume
that they are like a random sample from the populations of all American college men and women.
A comparison of the 95% confidence intervals for the population percentage will help us to make a generalization:

N ew ! Original Jo u r n a l
A r t i c l e s for 19 Examples and
Case Studies can be found on the
Student’s Suite CD-ROM. By reading the original, you are given the
opportunity to learn much more
about how the research was conducted, what statistical methods
were used, and what conclusions

the original researchers drew.

more songs from the same album will be among the first four songs played
in a random shuffle? We can find this by first determining the probability
that all of the first four songs of the shuffle are from different albums and
then subtracting that probability from 1. Define

What a Great Personality

Students in a statistics class at Penn State were asked, “Would you date
someone with a great personality even though you did not find them attractive?” By gender, the results were

●

xvii

.3

.4

.5
.6
Proportion who would

.7

Figure 10.5 ❚ 95% confidence intervals for proportions
who would date someone with a great personality who
wasn’t attractive

A 95% confidence interval for the difference is .035 to .334. It is entirely above 0, so we can conclude that the proportion of women in the population who would answer “yes” is probably higher than the proportion of
men who would do so.

The Development of Musical Preferences Will you always like the music that

you like now? If you are about 20 years old, the likely answer is “yes,” according to research reported in the Journal of Consumer Research (Holbrook and
Schindler, 1989). The researchers concluded that we tend to acquire our popular music preferences during late adolescence and early adulthood.
In the study, 108 participants from 16 to 86 years old each listened to 28 hit
songs that had been on Billboard’s Top 10 list for popular music some time between 1932 and 1986. Respondents rated the 28 songs on a 10-point scale, with
1 corresponding to “I dislike it a lot” and 10 corresponding to “I like it a lot.”
Each individual’s ratings were then adjusted so that the mean rating for each
participant was 0. On this adjusted rating scale, a positive score indicates a rating that was above average for a participant, whereas a negative score indicates
a below-average rating.


xviii

Preface: Getting Practice

Getting Practice
Updated! Basic Exerc i s e s , comprising 25% of all
exercises found in the text, focus
on practice and review; these exercises, indicated by a green circle
and appearing toward the beginning of each exercise section, complement the conceptual and dataanalysis exercises. Basic exercises
give you ample practice for these
key concepts.
Relevant conceptual E x e r c i s e s
have been added and updated
throughout the text. All exercises are
found at the end of each chapter,

with corresponding exercise sets
written for each section and chapter. You will find over 1500 exercises, allowing for ample opportunity to practice key concepts.

Exercises
●

◆

Denotes basic skills exercises
Denotes dataset is available in StatisticsNow at http://
1pass.thomson.com or on your CD but is not required to
solve the exercise.

Bold-numbered exercises have answers in the back of the text and
fully worked solutions in the Student Solutions Manual.
Go to the StatisticsNow website at
to:
• Assess your understanding of this chapter
• Check your readiness for an exam by taking the Pre-Test quiz and
exploring the resources in the Personalized Learning Plan

Section 7.1
7.1 ● According to a U.S. Department of Transportation
website ( 76.1% of
domestic flights flown by the top ten U.S. airlines from
June 1998 to May 1999 arrived on time. Represent this in
terms of a random circumstance and an associated
probability.
7.2 ● Jan is a member of a class with 20 students. Each day
for a week (Monday to Friday), a student in Jan’s class is

randomly selected to explain how to solve a homework
problem. Once a student has been selected, he or she is
not selected again that week. If Jan was not one of the
four students selected earlier in the week, what is the
probability that she will be picked on Friday? Explain
how you found your answer.
● Basic skills

Answers to Selected
E x e r c i s e s , indicated by bold
numbers in the Exercise sections,
have complete or partial solutions
found in the back of the text for
checking your answers and guiding
your thinking on similar exercises.

◆ Dataset available but not required

7.3 Identify three random circumstances in the following
story, and give the possible outcomes for each of them:
It was Robin’s birthday and she knew she was going
to have a good day. She was driving to work, and
when she turned on the radio, her favorite song was
playing. Besides, the traffic light at the main intersection she crossed to get to work was green when she
arrived, something that seemed to happen less than
once a week. When she arrived at work, rather than
having to search as she usually did, she found an
empty parking space right in front of the building.
7.4 Find information on a random circumstance in the
news. Identify the circumstance and possible outcomes, and assign probabilities to the outcomes. Explain how you determined the probabilities.

Section 7.2
7.5 ● Suppose you live in a city that has 125,000 households with telephones and a polling organization randomly selects 1000 of them to phone for a survey. What
is the probability that your household will be selected?
7.6 ● Is each of the following values a legitimate probability value? Explain any “no” answers.
a. .50
b. .00
c. 1.00
d. 1.25
e. Ϫ.25
Bold-numbered exercises answered in the back

Answers to Selected Exercises

The following are partial or complete answers to the exercises numbered in bold in the text.

Chapter 1
1.2
1.5
1.7
1.11
1.15
1.19
1.22
1.26

a. .00043
a. 400
c. Randomized experiment. d. Observational study.
189>11,034, or about 17>1000, based on placebo group.

a. 150 mph. b. 55 mph. c. 95 mph. d. 1>2 e. 51
No.
The base rate for that type of cancer.
a. 212>1525 ϭ .139. b. 1> 11525 ϭ .026.
c. .113 to .165.
1.28 a. Self-selected or volunteer. b. No. Readers with
strong opinions will respond.

Chapter 2
2.1
2.3
2.5

a.
a.
a.
c.
2.11 a.
c.

4 b. State in the United States. c. n ϭ 50.
Whole population. b. Sample.
Population parameter. b. Sample statistic.
Sample statistic.
Categorical. b. Quantitative.
Quantitative. d. Categorical.

2.34 a. Median is greater for males. b. Spread between
extremes is about the same; spread between quartiles is
slightly greater for males.

2.37 a. Skewed to the right. b. 13 ear pierces may be an
outlier. c. 2 ear pierces; about 45 women had this
number. d. About 32 or so.
2.39 a. Roughly symmetric. b. Highest ϭ 92.
2.44 Yes. Values inconsistent with the bulk of the data will be
obvious.
2.46 a. Shape is better evaluated by using a histogram.
2.48 Skewed to the left.
2.50 a. Median ϭ (72 ϩ 76)>2 ϭ 74; mean ϭ 74.33.
b. Median ϭ 7; mean ϭ 25.
2.52 a. Range ϭ 225 Ϫ 123 ϭ 102. b. IQR ϭ 35. c. 50%
2.53 a. Median ϭ 12.
2.54 d. There are no outliers.
2.59 The median is 16.72 inches. The data values vary from
6.14 to 37.42 inches. The middle 1>2 of the data is between 12.05 and 25.37 inches, so “typical” annual rainfall
covers quite a wide range.


Preface: Technology for Developing Concepts and Analyzing Data

xix

Technology for Developing Concepts
and Analyzing Data
Technology manuals, written specifically for Mind on Statistics, Third Edition,
walk you through the statistical software and graphing calculator—step by
step. You will find manuals for:
●

SPSS, written by Brenda K. Gunderson and Kirsten T. Namesnik at the

University of Michigan at Ann Arbor

●

MINITAB, written by Edith Seier and Robert M. Price, East Tennessee
State University

●

Excel, written by Tom Mason, University of St. Thomas

●

TI-83/84, written by Roger E. Davis, Pennsylvania College of Technology

N ew !

●

JMP IN, written by Jerry Reiter and Christine Kohnen, Duke University

N ew !

●

R, written by Mark A. Rizzardi, Humboldt State University

Note: These technology manuals are available in both print and electronic formats. Instructors, contact your sales representative to find out how these manuals can be custom published for your course.
Datasets for examples
a n d e x e r c i s e s are formatted

for MINITAB, Excel, SPSS, JMP,
SAS, R, TI-83/84, and ASCII.

N ew a n d U p d a t e d !
Minitab, Excel, TI-84,
a n d S P S S T i p s offer key
details on the use of technology.
TI-84 and SPSS Tips are new to
this edition.

MINITAB t ip

Calculating a Confidence Inter val for a Proportion
●

To compute a confidence interval for a proportion, use StatbBasic Statisticsb1 Proportion. (This procedure is not in versions earlier than Version 12.)

●

If the raw data are in a column of the worksheet, specify that column. If the
data have already been summarized, click on “Summarized Data,” and
then specify the sample size and the count of how many observations
have the characteristic of interest.

Note: To calculate intervals in the manner described in this chapter, use
the Options button, and click on “Use test and interval based on normal distribution.” Note also that the confidence level can be changed by using the
Options button.


xx

Preface: Tools for Review

Tools for Review
Updated! In Summary
boxes serve as a useful study tool,
appearing at appropriate points
to enhance key concepts and calculations. A complete list of these
can be found in StatisticsNow.

Key Te r m s at the end of each
chapter, organized by section, can
be used as a “quick-finder” and as
a review tool.

in summ ar y

Possible Reasons for Outliers
and Reasonable Actions
●

The outlier is a legitimate data value and represents natural variability for
the group and variable(s) measured. Values may not be discarded in this
case—they provide important information about location and spread.

●

A mistake was made while taking a measurement or entering it into the computer. If this can be verified, the values should be discarded or corrected.

●

The individual in question belongs to a different group than the bulk of individuals measured. Values may be discarded if a summary is desired and
reported for the majority group only.

Key Terms
Section 10.1
statistical inference, 401
confidence interval, 401, 403, 405, 408
sampling distribution, 403

Section 10.2
unit, 403
population, 403
universe, 403
sample, 404
sample size, 404
population parameter, 404
sample statistic, 404, 408
sample estimate, 404, 408
point estimate, 404

N ew !

Fundamental Rule for Using Data for
Inference, 404, 414 – 415
interval estimate, 405, 408
confidence level, 405, 406
multiplier, 408
standard error of the sample statistic,
408

Section 10.3
confidence interval for a population
proportion p, 412, 414
standard error of a sample proportion,
412, 414, 417
margin of error, 420
conservative margin of error, 420

margin of error for a sample proportion,
420
approximate 95% confidence interval
for a proportion p, 420
conservative 95% confidence interval
for p, 421
conservative estimate of the margin of
error, 422

Section 10.4
confidence interval, difference between
two population proportions, 423, 424
confidence interval for p1 Ϫ p2, 424

Section 10.5
confidence intervals and decisions, 428

is a personalized learning companion that helps you gauge
your unique study needs and makes the most of your study time by building
focused, chapter by chapter, Personalized Learning Plans that reinforce key
concepts.

●

Pre-Tests, developed by Deborah Rumsey of The Ohio State University,
give you an initial assessment of your knowledge.

●

Personalized Learning Plans, based on your answers to the pre-test questions, outline key elements for review.

●

Post-Tests, also developed by Deborah Rumsey of The Ohio State University, assess your mastery of core chapter concepts; results can be
emailed to your instructor.

Note: StatisticsNow also serves as a one-stop portal for many of your Mind on
Statistics resources which are also found on the Student’s Suite CD, as well as
the Interactive Video Skillbuilder CD. Throughout the text, StatisticsNow
icons have been thoughtfully placed to direct you to the resources you need
when you need them.


Preface: Tools for Active Learning
N ew !

xxi

Interactive Video Skill Builder CD-ROM contains hours of helpful, interactive
video instruction presented by Lisa Sullivan of Boston University. Watch as she
walks you through key examples from the text, step by step—giving you a
foundation in the skills you need to know. Each example found on the CD,

as well as StatisticsNow, is identified by the StatisticsNow icon located in the
margin. Think of it as portable office hours!
vMentor™ allows you to talk (using your own computer microphone) to tutors
who will skillfully guide you through a problem using an interactive whiteboard
for illustration. Up to 40 hours of live tutoring a week is available with every new
book and can be accessed through .
The Book Companion Website offers book- and course-specific resources, such
as tutorial quizzes for each chapter and datasets for exercises. You can access the
website through the Student’s Suite CD or through .
The Student Solutions Manual, prepared by Jessica M. Utts and Robert F.
Heckard, provides worked-out solutions to selected problems in the text. This
is available for purchase through your local bookstore, as well as at http://1pass
.thomson.com.

Tools for Active Learning
Updated!

Skillbuilder Applet sections, previously called “Turn on Your Computer,” provide opportunities for in-class or independent hands-on exploration of key statistical concepts. The applets that accompany this feature can be found on the
Student’s Suite CD or at .

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The Activities Manual, written by Jessica M. Utts and Robert F. Heckard, includes a variety of activities for students to explore individually or in teams.
These activities guide students through key features of the text, help them
understand statistical concepts, provide hands-on data collection and interpretation team-work, include exercises with tips incorporated for solution strategies, and provide bonus dataset activities.

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JoinIn™ on Turning Point® offers instructors text-specific JoinIn content for
electronic response systems, prepared by Brenda K. Gunderson and Kirsten T.

Namesnik at the University of Michigan at Ann Arbor. You can transform your
classroom and assess students’ progress with instant in-class quizzes and polls.
Turning Point software lets you pose book-specific questions and display students’ answers seamlessly within Microsoft PowerPoint lecture slides, in conjunction with a choice of “clicker” hardware. Enhance how your students interact with you, your lecture, and each other.
Internet Companion for Statistics, written by Michael Larsen of Iowa State
University, offers practical information on how to use the Internet to increase
students’ understanding of statistics. Organized by key topics covered in the introductory course, the text offers a brief review of a topic, listings of appropriate
websites, and study questions designed to build students’ analytical skills. This
can be accessed through .