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 E
Trang 2Chapter 1
Case Study 1.1 Who Are Those Speedy Drivers? 2
Case Study 1.2 Safety in the Skies? 3
Case Study 1.3 Did Anyone Ask Whom You’ve Been Dating? 3
Case Study 1.4 Who Are Those Angry Women? 4
Case Study 1.5 Does Prayer Lower Blood Pressure? 5
Case Study 1.6 Does Aspirin Reduce Heart Attack Rates? 5
Case Study 1.7 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
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
*Examples marked by an asterisk are revisited for further discussion later in the chapter.
Trang 3Gambler 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 ⴚ2.59 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 The “Freshman 15” 334
Example 9.2 Opinions About Genetically Modified Food 339
Example 9.3 Probability of Quitting with a Nicotine Patch 339
Example 9.4 How Much More Likely Are Smokers to Quit with a Nicotine Patch? 340
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
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 nⴝ 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 Are Side Effects Experienced by Fewer Than 20% of Patients? 497 Example 12.2 Does a Majority Favor the Proposed Blood Alcohol Limit? 498 Example 12.3 Psychic Powers 499
Example 12.4 Stop the Pain before It Starts 500 Example 12.5 A Jury Trial 504
Example 12.6 Errors in the Courtroom 504 Example 12.7 Errors in Medical Tests 505 Example 12.8 Calcium and the Relief of Premenstrual Symptoms 506
Trang 4*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 R2 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 Classroom Seat Location and Grade Point Average 670
Example 16.2 Application of Notation to the GPA and Classroom Seat Sample 672
Example 16.3 Assessing the Necessary Conditions for the GPA and Seat Location Data 673
Example 16.4 Occupational Choice and Testosterone Level 674
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 Playing the Lottery 710 Example 17.2 Surgery or Uncertainty? 710 Example 17.3 Fish Oil and Psychiatric Disorders 711 Example 17.4 Go, Granny, Go or Stop, Granny, Stop? 713 Example 17.5 When Smokers Butt Out, Does Society Benefit? 714 Example 17.6 Is It Wining or Dining That Helps French Hearts? 716 Example 17.7 Give Her the Car Keys 717
Example 17.8 Lifestyle Statistics from the Census Bureau 718 Example 17.9 In Whom Do We Trust? 719
Supplemental Topic 1
*Example S1.1 Random Security Screening S1-3
*Example S1.2 Betting Birthdays for the Lottery S1-3 Example S1.3 Customers Entering a Small Shop S1-8 Example S1.4 Earthquakes in the Coming Year S1-10 Example S1.5 Emergency Calls to a Small Town Police Department S1-10 Example S1.6 Are There Illegal Drugs in the Next 5000 Cars? S1-11 Example S1.7 Calling On the Back of the Class S1-13
Supplemental Topic 2
Example S2.1 Normal Human Body Temperature S2-5 Example S2.2 Heights of Male Students and Their Fathers S2-6
*Example S2.3 Estimating the Size of Canada’s Population S2-9
Example S2.4 Calculating Tⴙfor a Sample of Systolic Blood Pressures S2-13 Example S2.5 Difference Between Student Height and Mother’s Height for College Women S2-14 Example S2.6 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 Sleep Hours Based on Gender and Seat Location S4-2
*Example S4.2 Pulse Rates, Gender, and Smoking S4-6 Example S4.3 Nature Versus Nurture in IQ Scores S4-14 Example S4.4 Happy Faces and Restaurant Tips Revisited S4-16 Example S4.5 Does Smoking Lead to More Errors? S4-18
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Jessica M Utts and Robert F Heckard
Trang 7educator, guide, and friend —who launched our careers
in statistics and continues to share his vision.
Trang 81 Statistics Success Stories and Cautionary Tales 1
2 Turning Data Into Information 12
3 Sampling: Surveys and How to Ask Questions 70
4 Gathering Useful Data for Examining Relationships 116
5 Relationships Between Quantitative Variables 150
6 Relationships Between Categorical Variables 192
7 Probability 228
8 Random Variables 278
9 Understanding Sampling Distributions: Statistics
as Random Variables 330
10 Estimating Proportions with Confidence 400
11 Estimating Means with Confidence 442
12 Testing Hypotheses About Proportions 494
13 Testing Hypotheses About Means 550
14 Inference About Simple Regression 598
15 More About Inference for Categorical Variables 634
16 Analysis of Variance 668
17 Turning Information Into Wisdom 704
iv
Brief Contents
Trang 91 Statistics Success Stories
1.1 What Is Statistics? 1
1.2 Seven Statistical Stories with Morals 2
1.3 The Common Elements in the Seven Stories 7
2.3 Summarizing One or Two Categorical Variables 19
2.4 Exploring Features of Quantitative Data with Pictures 242.5 Numerical Summaries of Quantitative Variables 36
2.6 How to Handle Outliers 47
2.7 Features of Bell-Shaped Distributions 49
2.8 Skillbuilder Applet: The Empirical Rule in Action 56
Key Terms 57
Exercises 58
3.1 Collecting and Using Sample Data Wisely 71
3.2 Margin of Error, Confidence Intervals, and Sample Size 753.3 Choosing a Simple Random Sample 80
3.4 Other Sampling Methods 83
3.5 Difficulties and Disasters in Sampling 89
Contents
v
Trang 103.6 How to Ask Survey Questions 953.7 Skillbuilder Applet: Random Sampling in Action 103Key Terms 106
Exercises 106
4.1 Speaking the Language of Research Studies 1174.2 Designing a Good Experiment 124
4.3 Designing a Good Observational Study 1334.4 Difficulties and Disasters in Experiments and Observational Studies 136
Key Terms 141Exercises 142
5.1 Looking for Patterns with Scatterplots 1525.2 Describing Linear Patterns with a Regression Line 1575.3 Measuring Strength and Direction with Correlation 1655.4 Regression and Correlation Difficulties and Disasters 1715.5 Correlation Does Not Prove Causation 176
5.6 Skillbuilder Applet: Exploring Correlation 178Key Terms 181
Exercises 217
7.1 Random Circumstances 2297.2 Interpretations of Probability 231
Trang 117.3 Probability Definitions and Relationships 238
7.4 Basic Rules for Finding Probabilities 243
7.5 Strategies for Finding Complicated Probabilities 251
7.6 Using Simulation to Estimate Probabilities 259
7.7 Flawed Intuitive Judgments About Probability 261
Key Terms 269
Exercises 269
8.1 What Is a Random Variable? 279
8.2 Discrete Random Variables 283
8.3 Expectations for Random Variables 288
8.4 Binomial Random Variables 294
8.5 Continuous Random Variables 300
8.6 Normal Random Variables 302
8.7 Approximating Binomial Distribution Probabilities 3118.8 Sums, Differences, and Combinations
of Random Variables 314
Key Terms 320
Exercises 321
9.1 Parameters, Statistics, and Statistical Inference 331
9.2 From Curiosity to Questions About Parameters 334
9.3 SD Module 0: An Overview of Sampling Distributions 3449.4 SD Module 1: Sampling Distribution for One Sample
Proportion 348
9.5 SD Module 2: Sampling Distribution for the Difference
in Two Sample Proportions 354
9.6 SD Module 3: Sampling Distribution for One
Sample Mean 357
9.7 SD Module 4: Sampling Distribution for the Sample Mean
of Paired Differences 362
9.8 SD Module 5: Sampling Distribution for the Difference
in Two Sample Means 365
9.9 Preparing for Statistical Inference:
Standardized Statistics 368
9.10 Generalizations Beyond the Big Five 373
9.11 Skillbuilder Applet: Finding the Pattern in Sample Means 379
Trang 12Key Terms 383Exercises 384
10.1 Introduction 40110.2 CI Module 0: An Overview of Confidence Intervals 40310.3 CI Module 1: Confidence Interval for a Population Proportion 410
10.4 CI Module 2: Confidence Intervals for the Difference
in Two Population Proportions 42310.5 Using Confidence Intervals to Guide Decisions 428Key Terms 432
Exercises 433
11.1 Introduction to Confidence Intervals for Means 44411.2 CI Module 3: Confidence Interval for One
Population Mean 45211.3 CI Module 4: Confidence Interval for the Population Mean
of Paired Differences 46111.4 CI Module 5: Confidence Interval for the Difference in Two Population Means 466
11.5 Understanding Any Confidence Interval 47811.6 Skillbuilder Applet: The Confidence Level in Action 480Key Terms 483
Exercises 484
12.1 Introduction 49512.2 HT Module 0: An Overview of Hypothesis Testing 49612.3 HT Module 1: Testing Hypotheses About a Population Proportion 511
12.4 HT Module 2: Testing Hypotheses About the Difference
in Two Population Proportions 52712.5 Sample Size, Statistical Significance, and Practical Importance 533
Key Terms 538Exercises 538
Trang 1313 Testing Hypotheses About Means 550
13.1 Introduction to Hypothesis Tests for Means 551
13.2 HT Module 3: Testing Hypotheses about One
Population Mean 55313.3 HT Module 4: Testing Hypotheses about the Population Mean of Paired Differences 561
13.4 HT Module 5: Testing Hypotheses about the Difference
in Two Population Means 56513.5 The Relationship Between Significance Tests
and Confidence Intervals 57413.6 Choosing an Appropriate Inference Procedure 576
13.7 Effect Size 580
13.8 Evaluating Significance in Research Reports 585
Key Terms 587Exercises 588
14.1 Sample and Population Regression Models 600
14.2 Estimating the Standard Deviation for Regression 60514.3 Inference About the Slope of a Linear Regression 609
14.4 Predicting y and Estimating Mean y at a Specific x 61314.5 Checking Conditions for Using Regression Models
for Inference 619Key Terms 625Exercises 626
15.1 The Chi-Square Test for Two-Way Tables 635
15.2 Analyzing 2 2 Tables 646
15.3 Testing Hypotheses About One Categorical Variable:
Goodness-of-Fit 652Key Terms 658Exercises 658
16.1 Comparing Means with an ANOVA F-Test 669
16.2 Details of One-Way Analysis of Variance 679
Trang 1416.3 Other Methods for Comparing Populations 68516.4 Two-Way Analysis of Variance 689
Key Terms 693Exercises 693
17.1 Beyond the Data 70517.2 Transforming Uncertainty Into Wisdom 70917.3 Making Personal Decisions 709
17.4 Control of Societal Risks 71317.5 Understanding Our World 71517.6 Getting to Know You 71817.7 Words to the Wise 719Exercises 721
The Supplemental Topics are available on the Student’s Suite CD,
or print copies may be custom published.
SUPPLEMENTAL TOPIC 1 Additional Discrete Random
S1.1 Hypergeometric Distribution S1-2S1.2 Poisson Distribution S1-7
S1.3 Multinomial Distribution S1-11Key Terms S1-13
Trang 15S2.3 The Wilcoxon Signed-Rank Test S2-12S2.4 The Kruskal–Wallis Test S2-16
Key Terms S2-19Exercises S2-19
SUPPLEMENTAL TOPIC 3 Multiple Regression S3-1
S3.1 The Multiple Linear Regression Model S3-3S3.2 Inference About Multiple Regression Models S3-9S3.3 Checking Conditions for Multiple Linear Regression S3-14Key Terms S3-16
Exercises S3-16
SUPPLEMENTAL TOPIC 4 Two-Way Analysis of Variance S4-1
S4.1 Assumptions and Models for Two-Way ANOVA S4-2S4.2 Testing for Main Effects and Interactions S4-9Key Terms S4-20
Exercises S4-21
SUPPLEMENTAL TOPIC 5 Ethics S5-1
S5.1 Ethical Treatment of Human and Animal Participants S5-2S5.2 Assurance of Data Quality S5-9
S5.3 Appropriate Statistical Analysis S5-14S5.4 Fair Reporting of Results S5-16Key Terms S5-20
Exercises S5-21
Trang 17A Challenge
Before you continue, think about how you would answer the question in thefirst 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 interestsinclude topics to which statistics has made a useful contribution As written inChapter 17, “information developed through the use of statistics has enhancedour understanding of how life works, helped us learn about each other, allowedcontrol over some societal issues, and helped individuals make informed deci-sions There is almost no area of knowledge that has not been advanced by sta-tistical studies.”
Statistical methods have contributed to our understanding of health, chology, ecology, politics, music, lifestyle choices, business, commerce, anddozens of other topics A quick look through this book, especially Chapters 1and 17, should convince you of this Watch for the influences of statistics in yourdaily life as you learn this material
psy-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 nega-tive experience taking a statistics class in college We hope this book will help toovercome these misguided stereotypes
Let’s return to the two bullets at the beginning of this Preface Without ing, do you remember the diameter of the moon? Unless you already had a
look-xiii
Trang 18pretty good idea, or have an excellent memory for numbers, you probably don’tremember One premise of this book is that new material is much easier tolearn and remember if it is related to something interesting or previouslyknown The diameter of the moon is about the same as the air distance betweenAtlanta and Los Angeles, San Francisco and Chicago, London and Cairo, orMoscow and Madrid Picture the moon sitting between any of those pairs ofcities, and you are not likely to forget the size of the moon again Throughoutthis book, new material is presented in the context of interesting and usefulexamples The first and last chapters (1 and 17) are exclusively devoted to ex-amples and case studies, which illustrate the wisdom that can be generatedthrough 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 becauseit’s something you have experienced or verified for yourself It is not likely to besomething 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 courage you to think and question, rather than simply read and listen
en-New to this Edition
The biggest changes have been made in Chapters 9 to 13, containing the corematerial on sampling distributions and statistical inference The new organiza-tion presents the material in a modular, more flexible format There are sixmodules for each of the topics of sampling distributions, confidence intervals,and hypothesis testing The first module presents an introduction and the re-maining five modules each deal with a specific parameter, such as one mean,one proportion, or the difference in two means Chapter 9 covers sampling dis-tributions, 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 sons: pedagogy and practicality The new structure emphasizes the similarityamong the inference procedures for the five parameters discussed It allowsinstructors to illustrate that each procedure covered is a specific instance of thesame process We recognize that instructors have different preferences for theorder in which to cover inference topics For instance, some prefer to first coverall topics about proportions and then cover all topics about means Others pre-fer to first cover everything about confidence intervals and then cover every-thing about hypothesis testing With the new modular format, instructors cancover these topics in the order they prefer
rea-To aid in the navigation through these modular chapters, we have addedcolor-coded, labeled tabs that correspond to the introductory and parametermodules The table below, also found in Chapter 9, lays out the color-codingsystem as well as the flexibility of these new chapters In addition, the table is auseful course planning tool
Trang 19In response to feedback from users, some other chapters have been panded and reorganized Most notably, Chapters 3 and 4 have been essentiallyreversed, so that random samples and surveys are presented before the morecomplicated studies based on randomized experiments and observationalstudies.
ex-Furthermore, to add to the flexibility of topic coverage, Supplemental ics 1 to 5 on discrete random variables, nonparametric tests, multiple regres-sion, two-way ANOVA, and ethics are now available for use in both print andelectronic formats Instructors, please contact your sales representative to findout how these chapters can be custom published for your course
Top-Student Resources:
Tools for Expanded Learning
There are a number of tools provided in this book and beyond to enhance yourlearning of statistics
Tools for Conceptual Understanding
Organization of Chapters 9 to 13
0 Introductory SD Module 0 CI Module 0 HT Module 0
Overview of sampling Overview of confidence Overview of hypothesis
Proportion (p) SD for one sample CI for one population HT for one population
2 Difference in two SD Module 2 CI Module 2 HT Module 2
population proportions SD for difference in two CI for difference in two HT for difference in two
(p1ⴚ p2 ) sample proportions population proportions population proportions
SD for one sample mean CI for one population mean HT for one population mean
paired differences (Md) SD for sample mean of CI for population mean of HT for population mean of paired
paired differences paired differences differences
population means SD for difference in two CI for difference in two HT for difference in two
(M1ⴚ M2 ) sample means population means population means
Updated! Thought
Question boxes, previously
called “Turn on Your Mind,” appear
throughout each chapter to
encour-age active thinking and questioning
about statistical ideas Hints are
provided at the bottom of the page
to help you develop this skill.
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?*
thought ques tion 3.5
* 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?
Trang 20Investigating Real-Life Questions
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
fea-ture can be found on the Student’s
Suite CD or at http://1pass
.thomson.com.
skillbuilder applet9.11 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 approxi- mates 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 dif- ferent random samples from the same population Figure 9.13 illustrates the
Figure 9.13 ❚ The SampleMeans applet starting point
To explore this applet and work through this activity, go to Chapter 9
at http://1pass.thomson.com and
click on Skillbuilder Applet, or view the applet on your CD.
Updated! Technical
Notes boxes, previously
called “Tech Notes,” provide
additional technical discussion
of key concepts.
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 consump- tion, age, and so on.
techni cal note
Updated! Relevant
Ex-amples form the basis for
dis-cussion in each chapter and walk
you through real-life uses of
statisti-cal concepts.
E x a m p l e 9 1 The “Freshman 15” Do college students really gain weight during their
fresh-man 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 p proportion of the population of first-year college
stu-dents who weigh more at the end of the year than they did at the ning of the year.
begin-● Parameter md the mean (average) weight gain during the first year for
Trang 21case s tudy 10.3 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 attrac- tive?” By gender, the results were
● 61.1% of 131 women answered “yes.”
● 42.6% of 61 men answered “yes.”
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 ulations 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 col- lege men and women.
pop-A comparison of the 95% confidence intervals for the population centage will help us to make a generalization:
per-● For men, the approximate 95% confidence interval is 30.2% to 55%.
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.
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 tirely above 0, so we can conclude that the proportion of women in the pop- ulation who would answer “yes” is probably higher than the proportion of men who would do so.
en-Men Women
Proportion who would
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 ture 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.
fea-We mention the iPod because there has been much grumbling, ticularly 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
par-of songs by the same group or musician within the first hour or so par-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.
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
Event A first song is anything.
Event B second song is from different album than first.
C third song is from different album than first two picked.
D fourth song is from different album than first three picked The probability that all four are from different albums is
Thus, the probability that all four songs are not from different albums, ing that at least two songs are from the same album, is
mean-P(at least two of first four are from the same album) 1 53
47 P(A and B and C and D) 1201201081191189611784 53
Updated! Case Studies
apply statistical ideas to intriguing
news stories As the Case Studies
are developed, they model the
statistical reasoning process.
New! Original Journal
Ar ticles for 19 Examples and
Case Studies can be found on the
Student’s Suite CD-ROM By
read-ing the original, you are given the
opportunity to learn much more
about how the research was
con-ducted, what statistical methods
were used, and what conclusions
the original researchers drew.
Read the original source on your CD.
E x a m p l e 5 3 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,”
accord-ing to research reported in the Journal of Consumer Research (Holbrook and
Schindler, 1989) The researchers concluded that we tend to acquire our lar music preferences during late adolescence and early adulthood.
popu-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
be-tween 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 rat- ing that was above average for a participant, whereas a negative score indicates
Trang 22● 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
http://1pass.thomson.com 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 (http://www.dot.gov/airconsumer), 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.
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 intersec- tion 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 out- comes, and assign probabilities to the outcomes Ex- plain how you determined the probabilities.
Section 7.2
7.5 ● Suppose you live in a city that has 125,000 holds with telephones and a polling organization ran- domly selects 1000 of them to phone for a survey What
house-is the probability that your household will be selected? 7.6 ● Is each of the following values a legitimate probabil- ity value? Explain any “no” answers.
Updated! Basic
Exer-cises, comprising 25% of all
exercises found in the text, focus
on practice and review; these
exer-cises, indicated by a green circle
and appearing toward the
begin-ning of each exercise section,
com-plement the conceptual and
data-analysis exercises Basic exercises
give you ample practice for these
key concepts.
Relevant conceptual Exercises
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
chap-ter You will find over 1500
exer-cises, allowing for ample
opportu-nity to practice key concepts.
Answers to Selec ted
Exercises, 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.
Chapter 1
1.2 a .00043 1.5 a 400 1.7 c Randomized experiment d Observational study.
1.11 189>11,034, or about 17>1000, based on placebo group.
1.15 a 150 mph b 55 mph c 95 mph d 1>2 e 51 1.19 No.
1.22 The base rate for that type of cancer.
2.1 a 4 b State in the United States. c n 50.
2.3 a Whole population b Sample.
2.5 a Population parameter b Sample statistic.
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 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 (72 76)>2 74; mean 74.33.
b Median 7; mean 25.
2.52 a Range 225 123 102 b IQR 35 c 50% 2.53 a Median 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 be- tween 12.05 and 25.37 inches, so “typical” annual rainfall
Answers to Selected Exercises
The following are partial or complete answers to the exercises numbered in bold in the text.
Trang 23Technology 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 bystep You will find manuals for:
● SPSS, written by Brenda K Gunderson and Kirsten T Namesnik at theUniversity of Michigan at Ann Arbor
● MINITAB, written by Edith Seier and Robert M Price, East TennesseeState University
● Excel, written by Tom Mason, University of St Thomas
● TI-83/84, written by Roger E Davis, Pennsylvania College of Technology
● JMP IN, written by Jerry Reiter and Christine Kohnen, Duke University
● R, written by Mark A Rizzardi, Humboldt State University
Note: These technology manuals are available in both print and electronic
for-mats Instructors, contact your sales representative to find out how these uals can be custom published for your course
man-New and Updated!
Minitab, Excel, TI-84,
and SPSS Tips offer key
details on the use of technology
TI-84 and SPSS Tips are new to
this edition.
Calculating a Confidence Inter val for a Proportion
● To compute a confidence interval for a proportion, use Stat bBasic tistics b1 Proportion (This procedure is not in versions earlier than Ver- sion 12.)
Sta-● 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
dis-tribution.” Note also that the confidence level can be changed by using the
MINITAB tip
New!
New!
Datasets for examples
and exercises are formatted
for MINITAB, Excel, SPSS, JMP,
SAS, R, TI-83/84, and ASCII.
Trang 24New! is a personalized learning companion that helps you gauge
your unique study needs and makes the most of your study time by buildingfocused, chapter by chapter, Personalized Learning Plans that reinforce keyconcepts
● 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 tions, outline key elements for review
ques-● Post-Tests, also developed by Deborah Rumsey of The Ohio State versity, assess your mastery of core chapter concepts; results can beemailed to your instructor
Uni-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, StatisticsNowicons have been thoughtfully placed to direct you to the resources you needwhen you need them
Tools for Review
Updated! In Summar y
boxes serve as a useful study tool,
appearing at appropriate points
to enhance key concepts and
cal-culations A complete list of these
can be found in StatisticsNow.
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 puter If this can be verified, the values should be discarded or corrected.
com-● The individual in question belongs to a different group than the bulk of dividuals measured Values may be discarded if a summary is desired and
in-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
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 p 1 p 2 , 424
Section 10.5
confidence intervals and decisions, 428
Key Terms at the end of each
chapter, organized by section, can
be used as a “quick-finder” and as
a review tool.
Trang 25New! Interactive Video Skill Builder CD-ROM contains hours of helpful, interactive
video instruction presented by Lisa Sullivan of Boston University Watch as shewalks you through key examples from the text, step by step— giving you afoundation 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 themargin Think of it as portable office hours!
vMentor™ allows you to talk (using your own computer microphone) to tutorswho will skillfully guide you through a problem using an interactive whiteboardfor illustration Up to 40 hours of live tutoring a week is available with every newbook and can be accessed through http://1pass.thomson.com
The Book Companion Website offers book- and course-specific resources, such
as tutorial quizzes for each chapter and datasets for exercises You can access thewebsite through the Student’s Suite CD or through http://1pass.thomson.com.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,”
pro-vide opportunities for in-class or independent hands-on exploration of key tistical concepts The applets that accompany this feature can be found on theStudent’s Suite CD or at http://1pass.thomson.com
sta-New! The Activities Manual, written by Jessica M Utts and Robert F Heckard,
in-cludes a variety of activities for students to explore individually or in teams.These activities guide students through key features of the text, help themunderstand statistical concepts, provide hands-on data collection and interpre-tation team-work, include exercises with tips incorporated for solution strate-gies, and provide bonus dataset activities
New! 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 yourclassroom and assess students’ progress with instant in-class quizzes and polls.Turning Point software lets you pose book-specific questions and display stu-dents’ answers seamlessly within Microsoft PowerPoint lecture slides, in con-junction with a choice of “clicker” hardware Enhance how your students inter-act with you, your lecture, and each other
Internet Companion for Statistics, written by Michael Larsen of Iowa StateUniversity, offers practical information on how to use the Internet to increasestudents’ understanding of statistics Organized by key topics covered in the in-troductory course, the text offers a brief review of a topic, listings of appropriatewebsites, and study questions designed to build students’ analytical skills Thiscan be accessed through http://1pass.thomson.com
Trang 26Tools for Online Learning
CyberStats contains complete online content for your introductory statisticscourse It promotes learning through interaction on the Web and can be used
as the sole text for a course or in conjunction with a traditional text Students ternalize the behavior of statistical concepts by interacting with hundreds ofapplets (simulations and calculations) and receiving immediate feedback onpractice items Effective for both distance and on campus courses, CyberStatsprovides a learning opportunity that cannot be delivered in print Instructorsinterested in using this for their course should contact their local sales repre-sentative CyberStats is available for purchase by students through http://1pass.thomson.com
in-Instructor Resources:
Tools for Assessment
iLrn Statistics is your system for homework, integrated testing, and coursemanagement on the Web Using iLrn, instructors can easily set up onlinecourses, assign tests, quizzes, and homework, as well as monitor student prog-ress, enabling them to mentor students on the right points at the right time Stu-dent responses are automatically graded and entered into the iLrn grade book,making it easy for you to assign and collect homework or offer testing over theWeb Accessed through http://1pass.thomson.com and available with eachnew text, iLrn Statistics is comprised of two parts:
● iLrn Testing, containing algorithmically generated test items, can beused for testing, homework, or quizzing You choose! Instructors, contactyour sales representative to find out how to get access to this valuableresource
● iLrn Homework, which contains the exercises from the book, facilitatesclassroom management and assesses students through homework, onquizzes or on exams, in the process of doing real data analysis on the Web
Updated! The Test Bank, written by Brenda K Gunderson and Kirsten T Namesnik, of the
University of Michigan at Ann Arbor, includes test questions by section and ismade up of a combination of multiple-choice and free-response questions
Student Access Information
To summarize, many of the items mentioned above are available either on
a CD or at http://1pass.thomson.com via your 1pass access code, which isbound in every new text If you did not purchase this book new or you pur-chased a Basic Select text— containing no media resources—but would like touse any of the items described above, visit us online at http://1pass.thomson.com to purchase any of these valuable resources The following chart summa-rizes the access information
Updated!
Trang 27Student’s Interactive Video
InfoTrac ® College Edition ✔
Michael Larsen’s Internet Companion ✔
The entire Mind on Statistics learning package has been informed by the
rec-ommendations put forth by the ASA/MAA Joint Curriculum Committee andGAISE (Guidelines for Assessment and Instruction in Statistics Education).Each of the pedagogical features and ancillaries listed in the section entitled
“Student Resources: Tools for Expanded Learning” and “Instructor Resources:Tools for Assessment” have been categorized by suggested use to provide youwith options for designing a course that best fits the needs of your students
In addition to these tools you will also have access to the Instructor’s SuiteCD-ROM, which includes everything featured on the Student’s Suite CD-ROMplus the following:
● Course Outlines and Syllabi
● Class Projects
● Instructor’s Additional Resources
● Suggested discussions for the Thought Questions located throughoutthe text
● Multimedia manager containing all of the figures from the book in Point and as jpg files
Power-● Complete Solutions Manual
● Test Bank in Microsoft Word format
● Supplemental Topic Solutions
● List of Applications and Methods
Trang 28We thank William Harkness, Professor of Statistics at Penn State University, forcontinued support and feedback throughout our careers and during the writing
of this book, and for his remarkable dedication to undergraduate statistics
edu-cation Preliminary editions of Mind on Statistics, the basis for this text, were
used at Penn State, the University of California at Davis, and Texas A & M versity, and we thank the many students who provided comments and sugges-tions on those and on subsequent editions Thanks to Dr Melvin Morse (ValleyChildren’s Clinic and University of Washington) for suggesting the title forChapter 17 and to Deb Niemeier, University of California, Davis, for suggestingthat we add a supplemental chapter on Ethics (on the CD) We are indebted toNeal Rogness, Grand Valley State University, for help with the SPSS Tips, andLarry Schroeder and Darrell Clevidence, Carl Sandburg College, for help withthe TI-84 Tips; these tips are new to this edition At Penn State, Dave Hunter,Steve Arnold, and Tom Hettmansperger have provided many helpful insights
Uni-At the University of California at Davis, Rodney Wong has provided insights aswell as material for some exercises and the Test Bank We extend special thanks
to Phyllis Curtiss, Grand Valley State University, who provided hundreds ofvaluable suggestions for improving this edition of the book
The following reviewers offered valuable suggestions for this and previouseditions:
Patti B Collings, Brigham YoungUniversity
Elizabeth Clarkson, Wichita StateUniversity
James Curl, Modesto Junior CollegeWade Ellis, West Valley CollegeLinda Ernst, Mt Hood CommunityCollege
Joan Garfield, University ofMinnesota
Jonathan Graham, University ofMontana
Jay Gregg, Colorado State UniversityDonnie Hallstone, Green RiverCommunity College
Donald Harden, Georgia StateUniversity
Rosemary Hirschfelder, UniversitySound
Sue Holt, Cabrillo CommunityCollege
Mark Johnson, University of CentralFlorida
Tom Johnson, North CarolinaUniversity
Andre Mack, Austin CommunityCollege
D’Arcy Mays, Virginia wealth
Common-Megan Meece, University of FloridaMary Murphy, Texas A & M
UniversityEmily Murphree, Miami UniversityHelen Noble, San Diego StateUniversity
Thomas Nygren, Ohio StateUniversity
Nancy Pfenning, University ofPittsburgh
David Robinson, St Cloud StateUniversity
Neal Rogness, Grand Valley StateUniversity
Kelly Sakkinen, Lansing CommunityCollege
Heather Sasinouska, ClemsonUniversity
Kirk Steinhorst, University of IdahoGwen Terwilliger, University ofToledo
Robert Alan Wolf, University of SanFrancisco
Trang 29Finally, our sincere appreciation and gratitude goes to Carolyn Crockett,Danielle Derbenti, and the staff of Duxbury, without whom this book could nothave been written, and to Martha Emry who kept us on track throughout the ed-iting and production of all three editions of the book Finally, for their support,patience, and numerous prepared dinners, we thank our families and friends,especially Candace Heckard, Molly Heckard, Wes Johnson, Claudia Utts-Smith,and Dennis Smith.
Jessica Utts Robert Heckard
Trang 30Is a male or a female more likely to be behind
the wheel of this speeding car?
S e e C a s e S t u d y 1 1 (p 2)
Trang 31Let’s face it You’re a busy person Why should you spend your time
learning about a subject that sounds as dull as statistics? In this ter, we give seven examples of situations in which statistics either pro-vided enlightenment or misinformation With these examples, we hope
chap-to convince you that learning about this subject will be interesting anduseful
Each of the stories in this chapter illustrates one or more concepts thatwill be developed throughout the book These concepts are given as “themoral of the story” after a case is presented Definitions of some termsused in the story also are provided following each case By the time youread all of these stories, you already will have an overview of what statis-tics is all about ❚
1.1 What Is Statistics?
When you hear the word statistics you probably think of lifeless or gruesome
numbers, such as the population of your state or the number of violent crimes
committed in your city last year The word statistics, however, actually is used
to mean two different things The better-known definition is that statistics arenumbers measured for some purpose A more complete definition, and the onethat forms the substance of this book, is the following:
The stories in this chapter are meant to bring life to this definition When youare finished reading them, if you still think the subject of statistics is lifeless orgruesome, check your pulse!
Statistics Success Stories
and Cautionary Tales
The seven stories in this chapter are meant to bring life to the term statistics When you
are finished reading these stories, if you still think the subject of statistics is lifeless orgruesome, check your pulse!
Throughout the chapter, this icon
introduces a list of resources on the
StatisticsNow website at http://
1pass.thomson.com that will:
• Help you evaluate your knowledge
of the material
• Allow you to take an
exam-prep quiz
• Provide a Personalized Learning
Plan targeting resources that
address areas you should study
Statistics is a collection of procedures and principles for gathering dataand analyzing information to help people make decisions when faced withuncertainty
definition
Trang 321.2 Seven Statistical Stories with Morals
The best way to gain an understanding of some of the ideas and methods used
in statistical studies is to see them in action Each of the seven stories presented
in this section includes interesting lessons about how to gain information fromdata The methods and ideas will be expanded throughout the book, but theseseven stories will give you an excellent overview of why it is useful to study sta-tistics To help you understand some basic statistical principles, each case study
is accompanied by a “moral of the story” and by some definitions All of theideas and definitions will be discussed in greater detail in subsequent chapters
case study 1.1 Who Are Those Speedy Drivers?
A survey taken in a large statistics class at Penn State University contained
the question “What’s the fastest you have ever driven a car? mph.”
The data provided by the 87 males and 102 females who responded are
From these numbers, can you tell which sex tends to have driven faster and
by how much? Notice how difficult it is to make sense of the data when you
are simply presented with a list Even if the numbers had been presented in
numerical order, it would be difficult to compare the two sexes
Your first lesson in statistics is how to formulate a simple summary of
a long list of numbers The dotplot shown in Figure 1.1 helps us see the
pattern in the data In the plot, each dot represents the response of an
in-dividual student We can see that the men tend to claim a higher “fastest
ever driven” speed than do the women
The graph shows us a lot, and calculating some statistics that
summa-rize the data will provide additional insight There are a variety of ways to
do so, but for this example, we examine a five-number summary of the
data for males and females The five numbers are the lowest value; the
cut-off points for 1/4, 1/2, and 3/4 of the data; and the highest value Thethree middle values of the summary (the cutoff points for 1/4, 1/2, and
3/4 of the data) are called the lower quartile, median, and upper quartile,
respectively Five-number summaries can be represented like this:
Trang 33cities, and so on) The median of a numerical list of data is the value in
the middle when the numbers are put in order For an even number of ties, the median is the average of the middle two values The lower quar- tile and upper quartile are (roughly) the medians of the lower and up-
enti-per halves of the data
Moral of the Story: Simple summaries of data can tell an
interest-ing story and are easier to digest than long lists.
Definitions: Data is a plural word referring to numbers or
nonnumer-ical labels (such as male/female) collected from a set of entities (people,
case study 1.2 Safety in the Skies?
Air travelers already have enough concerns, with the potential for delays,
lost baggage, and so on So if you do a lot of air travel or know anyone who
does, you may have been disturbed by the headline in USA Today that read,
“Planes get closer in midair as traffic control errors rise” (Levin, 1999) You
may have been even more disturbed by the details: “Errors by air traffic
con-trollers climbed from 746 in fiscal 1997 to 878 in fiscal 1998, an 18%
in-crease.” Don’t cancel your next vacation yet A look at the statistics indicates
that the news is actually pretty good! And there is some reassurance when
we are told that “most [errors] involve planes passing so far apart that there
is no danger of collision.”
The headline and details do sound ominous — all those errors and an
increase of almost 20%! If things continue at that rate, won’t your next flight
be quite likely to suffer from air traffic controller error? The answer is a
re-sounding “no,” which becomes obvious when we are told the base rate or
baseline risk for errors “The errors per million flights handled by controllers
climbed from 4.8 to 5.5.” So the original rate of errors in 1997, from which
the 18% increase was calculated, was fewer than 5 errors per million flights
Fortunately, the rates for the two years were provided in the story This
is not always the case in news reports of increases in rates For instance, an
article may say that the rate of a certain type of cancer is doubled if you eat
a certain unhealthful food But what good is that information unless youknow the actual risk? Doubling your chance of getting cancer from 1 in amillion to 2 in a million is trivial, but doubling your chance from 1 in 50 to
2 in 50 is not
Moral of the Story: When discussing the change in the rate or risk
of occurrence of something, make sure you also include the base rate or baseline risk.
Definitions: Therate at which something occurs is simply the
num-ber of times it occurs per numnum-ber of opportunities for it to occur In fiscal year
1998, the rate of air traffic controller errors was 5.5 per million flights The
risk of a bad outcome in the future can be estimated by using the past rate
for that outcome, if it is assumed the future is like the past Based on 1998data, the estimated risk of an error for any given flight in 1999 would be5.5/1,000,000, or 0000055 The base rate or baseline risk is the
rate or risk at a beginning time period or under specific conditions For stance, the base rate of air traffic controller errors was 4.8 per million flights
in-in fiscal year 1997
case study 1.3 Did Anyone Ask Whom You’ve Been Dating?
“According to a new USA Today/Gallup Poll of teenagers across the
coun-try, 57 percent of teens who go out on dates say they’ve been out with
someone of another race or ethnic group” (Peterson, 1997) That’s over
half of the dating teenagers, so of course it was natural for the headline
in the Sacramento Bee to read, “Interracial dates common among today’s
teenagers.” The article contained other information as well, such as “In
most cases, parents aren’t a major obstacle Sixty-four percent of teens say
their parents don’t mind that they date interracially, or wouldn’t mind if
teens constituted a random sample from the population of interest.
(continued)
Trang 34case study 1.4 Who Are Those Angry Women?
A well-conducted survey can be very informative, but a poorly conducted
one can be a complete disaster As an extreme example, Moore (1997,
p 11) reports that for her highly publicized book Women and Love, Shere
Hite (1987) sent questionnaires to 100,000 women asking about love,
sex, and relationships Only 4.5% of the women responded, and Hite used
those responses to write her book As Moore notes, “The women who
re-sponded were fed up with men and eager to fight them For example, 91%
of those who were divorced said that they had initiated the divorce The
anger of women toward men became the theme of the book.” Do you
think that women who were angry with men would be likely to answer
ques-tions about love relaques-tionships in the same way as the general population of
women?
The Hite sample exemplifies one of the most common problems with
surveys: The sample data may not represent the population Extensive
nonresponse from a random sample, or the use of a self-selected (i.e.,
all-volunteer) sample, will probably produce biased results Those who
volun-tarily respond to surveys tend to care about the issue and therefore havestronger and different opinions than those who do not respond
Moral of the Story: An unrepresentative sample, even a large one, tells you almost nothing about the population.
Definitions: Nonresponse bias can occur when many people who
are selected for the sample either do not respond at all or do not respond tosome of the key survey questions This may occur even when an appropri-ate random sample is selected and contacted The survey is then based on anonrepresentative sample, usually those who feel strongly about the issues.Some surveys don’t even attempt to contact a random sample but insteadask anyone who wishes to respond to do so Magazines, television stations,and Internet websites routinely conduct this kind of poll, and those who re-spond are called aself-selected sample or a volunteer sample In
most cases, this kind of sample tells you nothing about the larger tion at all; it tells you only about those who responded
popula-Polls and sample surveys are frequently used to assess public opinion
and to estimate population characteristics such as the percent of teens whohave dated interracially Many sophisticated methods have been developedthat allow pollsters to gain the information they need from a very small num-ber of individuals The trick is to know how to select those individuals InChapter 3, we examine a number of other strategies that are used to ensurethat sample surveys provide reliable information about populations
Moral of the Story: A representative sample of only a few thousand,
or perhaps even a few hundred, can give reasonably accurate information about a population of many millions.
Definitions: A population is a collection of all individuals about
which information is desired The “individuals” are usually people but couldalso be schools, cities, pet dogs, agricultural fields, and so on A random sample is a subset of the population selected so that every individual has
a specified probability of being part of the sample In a sample survey,
the investigators gather opinions or other information from each ual included in the sample The margin of error for a properly conducted
individ-survey is a number that is added to and subtracted from the sample mation to produce an interval that is 95% certain to contain the truth aboutthe population In the most common types of sample surveys, the mar-gin of error is approximately equal to 1 divided by the square root of thenumber of individuals in the sample Hence, a sample of 496 teenagerswho have dated produces a margin of error of about 1> 045, orabout 4.5%
infor-2496
The featured statistic of the article is that “57 percent of teens who go
out on dates say they’ve been out with someone of another race or ethnic
group.” Only 496 of the 602 teens in the poll said that they date, so the
57% value is actually a percentage based on 496 responses In other words,
the pollsters were using information from only 496 teenagers to estimate
something about all teenagers Figure 1.2 illustrates this situation
How accurate could this sample possibly be? The answer may surprise
you The results of this poll are accurate to within a margin of error of about
4.5% As surprising as it may seem, the true percentage of all teens in the
United States who date interracially is reasonably likely to be within 4.5%
of the reported percentage that’s based only on the 496 teens asked! We’ll
be conservative and round the 4.5% margin of error up to 5% The percent
of all teenagers in the United States who date that would say they have
dated someone of another race or ethnic group is likely to be in the range
57% 5%, or between 52% and 62% (The symbol is read “plus and
minus” and means that the value on the right should be added to and
sub-tracted from the value on the left to create an interval.)
Trang 35case study 1.5 Does Prayer Lower Blood Pressure?
Read the original source on your CD.
Newspaper headlines are notorious for making one of the most common
mis-takes in the interpretation of statistical studies: jumping to unwarranted
con-clusions A headline in USA Today read, “Prayer can lower blood pressure”
(Davis, 1998) The story that followed continued the possible fallacy it
be-gan by stating, “Attending religious services lowers blood pressure more
than tuning into religious TV or radio, a new study says.” The words
“at-tending religious services lowers blood pressure” imply a direct
cause-and-effect relationship This is a strong statement, but it is not a statement that
is justified by the research project described in the article
The article was based on an observational study conducted by the
U.S National Institutes of Health, which followed 2391 people aged 65 or
older for six years The article described one of the study’s principal findings:
“People who attended a religious service once a week and prayed or
stud-ied the Bible once a day were 40% less likely to have high blood pressure
than those who don’t go to church every week and prayed and studied the
Bible less” (Davis, 1998) So the researchers did observe a relationship, but
it’s a mistake to think that this justifies the conclusion that prayer actually
causes lower blood pressure.
When groups are compared in an observational study, the groups
usu-ally differ in many important ways that may contribute to the observed
rela-tionship In this example, people who attended church and prayed regularlymay have been less likely than the others to smoke or to drink alcohol Thesecould affect the results because smoking and alcohol use are both believed
to affect blood pressure The regular church attendees may have had a bettersocial network, a factor that could lead to reduced stress, which in turn couldreduce blood pressure People who were generally somewhat ill may not havebeen as willing or able to go out to church We’re sure you can think of other
possibilities for confounding variables that may have contributed to the
ob-served relationship between prayer and lower blood pressure
Moral of the Story: Cause-and-effect conclusions cannot generally
be made on the basis of an observational study.
Definitions:Anobservational study is one in which participants
are merely observed and measured Comparisons based on observationalstudies are comparisons of naturally occurring groups A variable is a char-
acteristic that differs from one individual to the next It may be numerical,such as blood pressure, or it may be categorical, such as whether or notsomeone attends church regularly A confounding variable is a variable
that is not the main concern of the study but may be partially responsible forthe observed results
case study 1.6 Does Aspirin Reduce Heart Attack Rates?
Read the original source on your CD.
In 1988, the Steering Committee of the Physicians’ Health Study Research
Group released the results of a five-year randomized experiment conducted
using 22,071 male physicians between the ages of 40 and 84 The purpose
of the experiment was to determine whether taking aspirin reduces the risk
of a heart attack The physicians had been randomly assigned to one of the
two treatment groups One group took an ordinary aspirin tablet every other
day, while the other group took a placebo None of the physicians knew
whether he was taking the actual aspirin or the placebo
The results, shown in Table 1.1, support the conclusion that taking
as-pirin does indeed help to reduce the risk of having a heart attack The rate
of heart attacks in the group taking aspirin was only about half the rate of
heart attacks in the placebo group In the aspirin group, there were 9.42
heart attacks per 1000 participating doctors, while in the placebo group,
there were 17.13 heart attacks per 1000 participants
Because the men in this experiment were randomly assigned to the twoconditions, other important risk factors such as age, amount of exercise, anddietary habits should have been similar for the two groups The only impor-tant difference between the two groups should have been whether they tookaspirin or a placebo This makes it possible to conclude that taking aspirin
actually caused the lower rate of heart attacks for that group In a later
chap-ter, you will learn how to determine that the difference seen in this sample
is statistically significant In other words, the observed sample difference
probably reflects a true difference within the population
(continued)
Table 1.1 The Effect of Aspirin on Heart Attacks
Trang 36Definitions: Arandomized experiment is a study in which
treat-ments are randomly assigned to participants A treatment is a specific
reg-imen or procedure assigned to participants by the experreg-imenter A random assignment is one in which each participant has a specified probability of
being assigned to each treatment A placebo is a pill or treatment designed
to look just like the active treatment but with no active ingredients A tistically significant relationship or difference is one that is large
sta-enough to be unlikely to have occurred in the sample if there was no tionship or difference in the population
rela-To what population does the conclusion of this study apply? The
par-ticipants were all male physicians, so the conclusion that aspirin reduces the
risk of a heart attack may not hold for the general population of men No
women were included, so the conclusion may not apply to women at all
More recent evidence, however, has provided additional support for the
ben-efit of aspirin in broader populations
Moral of the Story: Unlike with observational studies,
cause-and-effect conclusions can generally be made on the basis of randomized
experiments.
case study 1.7 Does the Internet Increase
Loneliness and Depression?
It was big news Researchers at Carnegie Mellon University had found that
“greater use of the Internet was associated with declines in participants’
communication with family members in the household, declines in size of
their social circle, and increases in their depression and loneliness” (Kraut
et al., 1998, p 1017) An article in the New York Times reporting on this
study was entitled “Sad, lonely world discovered in cyberspace” (Harmon,
1998) The study included 169 individuals in 73 households in Pittsburgh,
Pennsylvania, who were given free computers and Internet service in 1995
The participants answered a series of questions at the beginning of the study
and either one or two years later, measuring social contacts, stress,
loneli-ness, and depression The New York Times reported:
In the first concentrated study of the social and psychological effects of
Internet use at home, researchers at Carnegie Mellon University have
found that people who spend even a few hours a week online have higher
levels of depression and loneliness than they would if they used the
computer network less frequently it raises troubling questions about
the nature of “virtual” communication and the disembodied relationships
that are often formed in cyberspace
Source: “Sad, Lonely World Discovered in Cyberspace,” by A Harmon, New York Times, August 30,
1998, p A3 Reprinted with permission of the New York Times Company.
Given these dire reports, one would think that using the Internet for a few
hours a week is devastating to one’s mental health But a closer look at the
findings reveals that the changes were actually quite small, though
statisti-cally significant Internet use averaged 2.43 hours per week for participants
The number of people in the participants’ “local social network” decreased
from an average of 23.94 people to an average of 22.90 people, hardly
a noticeable loss On a scale from 1 to 5, self-reported loneliness decreased
from an average of 1.99 to 1.89 (lower scores indicate greater
loneli-ness) And on a scale from 0 to 3, self-reported depression dropped from
an average of 0.73 to an average of 0.62 (lower scores indicate higherdepression)
The New York Times did report the magnitude of some of the changes,
noting for instance that “one hour a week on the Internet was associated,
on average, with an increase of 0.03, or 1 percent on the depression scale.”But the attention the research received masked the fact that the impact ofInternet use on depression, loneliness, and social contact was actually quitesmall
As a follow-up to this study, in July 2001, USA Today (Elias, 2001)
re-ported that in continued research, the bad effects had mostly disappeared.The article, titled “Web use not always a downer: Study disputes link to de-pression,” began with the statement “Using the Internet at home doesn’tmake people more depressed and lonely after all.” However, the articlenoted that the lead researcher, Robert Kraut of Carnegie Mellon University,believes that the earlier findings were correct but that “the Net has become
a more social place since the study began in 1995.” His explanation for thechange in findings is that “either the Internet has changed, or people havelearned to use it more constructively, or both.” Whether the link ever existedwill never be known, but it is not surprising, given the small magnitude of theoriginal finding, that it subsequently disappeared
Moral of the Story: A “statistically significant” finding does not essarily have practical importance When a study reports a statistically significant finding, find out the magnitude of the relationship or difference A secondary moral to this story is that the implied direction of cause and effect may be wrong.
nec-In this case, it could be that people who were more lonely and depressed were more prone to using the Internet And as the follow-up research makes clear, remember that “truth” doesn’t necessarily remain fixed across time Any study should be viewed in the context of society at the time it was done.
Trang 371.3 The Common Elements
in the Seven Stories
The seven stories were meant to bring life to our definition of statistics Let’sconsider that definition again:
Statistics is a collection of procedures and principles for gathering dataand analyzing information to help people make decisions when facedwith uncertainty
Think back over the seven stories In every story, data are used to make a
judg-ment about a situation This common theme is what statistics is all about.
The Discovery of Knowledge
Each story illustrates part of the process of discovery of new knowledge, forwhich statistical methods can be very useful The basic steps in this process are
as follows:
1 Asking the right question(s).
2 Collecting useful data, which includes deciding how much is needed.
3 Summarizing and analyzing data, with the goal of answering the
questions
4 Making decisions and generalizations based on the observed data.
5 Turning the data and subsequent decisions into new knowledge.
We’ll explore these five steps throughout the book, concluding with a chapter
on “Turning Information into Wisdom.” We’re confident that your active ticipation in this exploration will benefit you in your everyday life and in yourfuture professional career
par-In a practical sense, almost all decisions in life are based on knowledge tained by gathering and assimilating data Sometimes the data are quantitative,
ob-as when an instructor must decide what grades to give on the bob-asis of a tion of homework and exam scores Sometimes the information is more quali-tative and the process of assimilating it is informal, such as when you decidewhat you are going to wear to a party In either case, the principles in this bookwill help you to understand how to be a better decision maker
collec-Think about a decision that you recently had to make What “data”did you use to help you make the decision? Did you have as much information as youwould have liked? If you could freely use them, how would you use the principles inthis chapter to help you gain more useful information?*
thought question 1.1
*H I N T : As an example, how did you decide to live where you are living? What additional data,
if any, would have been helpful?
Trang 38in summar y Some Important Statistical Principles
The “moral of the story” items for the case studies presented in this chaptergive a good overview of many of the important ideas covered in this book.Here is a summary:
● Simple summaries of data can tell an interesting story and are easier todigest than long lists
● When discussing the change in the rate or risk of occurrence of something,make sure you also include the base rate or baseline risk
● A representative sample of only a few thousand, or perhaps even a fewhundred, can give reasonably accurate information about a population ofmany millions
● An unrepresentative sample, even a large one, tells you almost nothingabout the population
● Cause-and-effect conclusions cannot generally be made on the basis of anobservational study
● Cause-and-effect conclusions can generally be made on the basis of domized experiments
ran-● A statistically significant finding does not necessarily have practical cance or importance When a study reports a statistically significant find-ing, find out the magnitude of the relationship or difference
Case Study 1.3
population, 3, 4 random sample, 3, 4 sample survey, 4 margin of error, 4
Case Study 1.4
nonresponse bias, 4 self-selected sample, 4 volunteer sample, 4
Case Study 1.5
observational study, 5 variable, 5
confounding variable, 5
Case Studies 1.6 and 1.7
randomized experiment, 5, 6 treatment, 5, 6
random assignment, 5, 6 placebo, 5, 6
statistically significant, 5, 6 and practical importance, 6, 7
Every term in this chapter is discussed more extensively in later chapters, so don’t worry if you don’t understand all of theterminology that has been introduced here The following list indicates the page number(s) where the important terms inthis chapter are introduced and defined
Trang 39● 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
http://1pass.thomson.com 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
Note: Many of these exercises will be repeated in later
chap-ters in which the relevant material is covered in more detail.
1.1 ● A five-number summary for the heights in inches of
the women who participated in the survey in Case
Study 1.1 is as shown.
a What is the median height for these women?
b What is the range of heights, that is, the difference
in heights between the shortest and the tallest
women?
c What is the interval of heights containing the
short-est 1 >4 of the women?
d What is the interval of heights containing the middle
1 >2 of the women?
1.2 ● In the year 2000, Vietnamese American women had
the highest rate of cervical cancer in the country
Sup-pose that among 200,000 Vietnamese American women,
86 developed cervical cancer.
a Calculate the rate of cervical cancer for these women.
b What is the estimated risk of developing cervical
can-cer for Vietnamese American women in the next year?
c Explain the conceptual difference between the rate
and the risk, in the context of this example.
1.3 ● Using Case Study 1.6 as an example, explain the
dif-ference between a population and a sample.
1.4 ● A telephone survey of 2000 Canadians conducted
March 20 –30, 2001, found that “Overall, about half of
Canadians in the poll say the right number of
immi-grants are coming into the country and that
immigra-tion has a positive effect on Canadian communities.
Only 16 percent view it as a negative impact while
one-third said it had no impact at all” (The Ottawa Citizen,
August 17, 2001, p A6.).
a What is the population for this survey?
b How many people were in the sample used for this survey?
c What is the approximate margin of error for this survey?
d Provide an interval of numbers that is 95% certain
to cover the true percentage of Canadians who view immigration as having a negative impact.
1.5 ● In Case Study 1.3, the margin of error for the sample
of 496 teenagers was about 4.5% How many teenagers should be in the sample to produce each of the follow- ing as the approximate margin of error?
a Margin of error 05 or 5%.
b Margin of error 30 or 30%.
1.6 ● A proposed study design is to leave 100 naires by the checkout line in a student cafeteria The questionnaire can be picked up by any student and returned to the cashier Explain why this volunteer sample is a poor study design.
question-1.7 ● For each of the examples given here, decide whether the study was an observational study or a randomized experiment.
a A group of 100 students was randomly divided, with
50 assigned to receive vitamin C and the remaining
50 to receive a placebo, to determine whether min C helps to prevent colds.
vita-b All patients who received a hip transplant tion at Stanford University Hospital during 1995 to
opera-2005 will be followed for 10 years after their ation to determine the success (or failure) of the transplant.
oper-c A group of students who were enrolled in an ductory statistics course were randomly assigned
intro-to take a Web-based course or intro-to take a traditional lecture course The two methods were compared
by giving the same final examination in both courses.
d A group of smokers and a group of nonsmokers who visited a particular clinic were asked to come in for a physical exam every 5 years for the rest of their lives
to monitor and compare their health status 1.8 ● Suppose that an observational study showed that students who get at least 7 hours of sleep performed better on exams than students who didn’t Which of the following are possible confounding variables, and which are not? Explain why in each case.
a Number of courses the student took that term.
b Weight of the student.
c Number of hours the student spent partying in a typical week.
1.9 ● Explain the distinction between statistical cance and practical significance Can the result of a study be statistically significant but not practically significant?
signifi-1.10 A headline in a major newspaper read, “Breast-Fed Youth Found to Do Better in School.”
● Basic skills ◆ Dataset available but not required Bold-numbered exercises answered in the back
Exercises
Female Heights (Inches) Median 65
Quartiles 63.5 67.5
Extremes 59 71
Trang 40a Do you think this statement was based on an
ob-servational study or a randomized experiment?
Explain.
b Given your answer in part (a), which of these two
al-ternative headlines do you think would be
prefer-able: “Breast-Feeding Leads to Better School
Per-formance” or “Link Found Between Breast-Feeding
and School Performance”? Explain.
1.11 Refer to Case Study 1.6, in which the relationship
be-tween aspirin and heart attack rates was examined.
Using the results of this experiment, what do you think
is the base rate of heart attacks for men like the ones in
this study? Explain.
1.12 A random sample of 1001 University of California
fac-ulty members taken in December 1995 was asked, “Do
you favor or oppose using race, religion, sex, color,
ethnicity, or national origin as a criterion for
admis-sion to the University of California?” (Roper Center,
1996) Fifty-two percent responded “favor.”
a What is the population for this survey?
b What is the approximate margin of error for the
survey?
c Based on the results of the survey, could it be
con-cluded that a majority (over 50%) of all University
of California faculty members favor using these
cri-teria? Explain.
1.13 In this chapter, you learned that cause and effect can
be concluded from randomized experiments but
gen-erally not from observational studies Why don’t
re-searchers simply conduct all studies as randomized
experiments rather than observational studies?
1.14 Give an example of a question you would like to have
answered, such as whether eating chocolate helps to
prevent depression Then explain how a randomized
experiment or an observational study could be done to
study this question.
1.15 Refer to the data and five-number summaries given in
Case Study 1.1 Give a numerical value for each of the
following.
a The fastest speed driven by anyone in the class.
b The slowest speed driven by a male.
c The speed for which 1 >4 of the women had driven at
that speed or faster.
d The proportion of females who had driven 89 mph
or faster.
e The number of females who had driven 89 mph or
faster.
1.16 Why was the study described in Case Study 1.5
conducted as an observational study instead of an
experiment?
1.17 Students in a statistics class at Penn State were asked,
“About how many minutes do you typically exercise
in a week?” Responses from the women in the class were
b For each gender, determine the median response.
c Do you think there’s a “significant” difference tween the weekly amount that men and women ex- ercise? Explain.
1.19 Case Study 1.6 reported that the use of aspirin reduces the risk of heart attack and that the relationship was found to be “statistically significant.” Does either of the cautions in the “moral of the story” for Case Study 1.7 apply to this result? Explain.
1.20 An article in the magazine Science (Service, 1994)
dis-cussed a study comparing the health of 6000 ans and a similar number of their friends and relatives who were not vegetarians The vegetarians had a 28% lower death rate from heart attacks and a 39% lower death rate from cancer, even after the researchers ac- counted for differences in smoking, weight, and so- cial class In other words, the reported percentages were the remaining differences after adjusting for dif- ferences in death rates due to those factors.
vegetari-a Is this an observational study or a randomized periment? Explain.
ex-b On the basis of this information, can we conclude that a vegetarian diet causes lower death rates from heart attacks and cancer? Explain.
c Give an example of a potential confounding able and explain what it means to say that this is a confounding variable.
vari-1.21 Refer to Exercise 1.20, comparing vegetarians and vegetarians for two causes of death Were base rates given for the two causes of death? If so, what were they? If not, explain what a base rate would be for this study.
non-1.22 An article in the Sacramento Bee (March 8, 1984, p A1)
reported on a study finding that “men who drank 500 ounces or more of beer a month (about 16 ounces a day) were three times more likely to develop cancer of the rectum than nondrinkers.” In other words, the rate
of cancer was triple in the beer-drinking group when compared with the nonbeer drinkers in this study What important numerical information is missing from this report?
1.23 Dr Richard Hurt and his colleagues (Hurt et al., 1994) randomly assigned volunteers wanting to quit smok- ing to wear either a nicotine patch or a placebo patch
to determine whether wearing a nicotine patch proves the chance of quitting After 8 weeks of use, 46%
im-of those wearing the nicotine patch but only 20% im-of those wearing the placebo patch had quit smoking.
a Was this a randomized experiment or an tional study?
observa-● Basic skills ◆ Dataset available but not required Bold-numbered exercises answered in the back