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Mind on statistics 4th edition.pdf
Cover Page
Case Studies and Examples
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Supplemental Topic 1
Supplemental Topic 2
Supplemental Topic 3
Supplemental Topic 4
Supplemental Topic 5
Half-Title Page
Title Page
Copyright Page
Dedication Page
Brief Contents
Contents
Preface
A Challenge
What Is Statistics, and Who Should Care?
How Is This Book Different? Two Basic Premises of Learning
New to This Edition
Text Features
Student Resources: Tools for Learning
Tools for Conceptual Understanding
Investigating Real-Life Questions
Getting Practice
Technology for Developing Concepts and Analyzing Data
Tools for Review
Tools for Active Learning
Instructor Resources: Tools for Assessment
A Note to Instructors
Acknowledgments
Chapter 1: Statistics Success Stories and Cautionary Tales
1.1: What Is Statistics?
1.2: Eight Statistical Stories with Morals
1.3: The Common Elements in the Eight Stories
Key Terms
In Summary box
Exercises
Chapter 2: Turning Data into Information
2.1: Raw Data
2.2: Types of Variables
2.3: Summarizing One or Two Categorical Variables
2.4: Exploring Features of Quantitative Data with Pictures
2.5: Numerical Summaries of Quantitative Variables
2.6: How to Handle Outliers
2.7: Bell-Shaped Distributions and Standard Deviations
2.8: The Empirical Rule in Action
Key Terms
In Summary Boxes
Exercises
Chapter 3: Relationships Between Quantitative Variables
3.1: Looking for Patterns with Scatterplots
3.2: Describing Linear Patterns with a Regression Line
3.3: Measuring Strength and Direction with Correlation
3.4: Regression and Correlation Difficulties and Disasters
3.5: Correlation Does Not Prove Causation
3.6: Exploring Correlation
Key Terms
In Summary Box
Exercises
Chapter 4: Relationships Between Categorical Variables
4.1: Displaying Relationships Between Categorical Variables
4.2: Risk, Relative Risk, and Misleading Statistics about Risk
4.3: The Effect of a Third Variable and Simpson’s Paradox
4.4: Assessing the Statistical Significance of a 2X2 Table
Key Terms
In Summary Boxes
Exercises
Chapter 5: Sampling: Surveys and How to Ask Questions
5.1: Collecting and Using Sample Data Wisely
5.2: Margin of Error, Confidence Intervals, and Sample Size
5.3: Choosing a Simple Random Sample
5.4: Other Sampling Methods
5.5: Difficulties and Disasters in Sampling
5.6: How to Ask Survey Questions
5.7: Random Sampling in Action
Key Terms
In Summary Boxes
Exercises
Chapter 6: Gathering Useful Data for Examining Relationships
6.1: Speaking the Language of Research Studies
6.2: Designing a Good Experiment
6.3: Designing a Good Observational Study
6.4: Difficulties and Disasters in Experiments and Observational Studies
Key Terms
In Summary Boxes
Exercises
Chapter 7: Probability
7.1: Random Circumstances
7.2: Interpretations of Probability
7.3: Probability Definitions and Relationships
7.4: Basic Rules for Finding Probabilities
7.5: Finding Complicated Probabilities
7.6: Using Simulation to Estimate Probabilities
7.7: Flawed Intuitive Judgments about Probability
Key Terms
In Summary Boxes
Exercises
Chapter 8: Random Variables
8.1: What Is a Random Variable?
8.2: Discrete Random Variables
8.3: Expectations for Random Variables
8.4: Binomial Random Variables
8.5: Continuous Random Variables
8.6: Normal Random Variables
8.7: Approximating Binomial Distribution Probabilities
8.8: Sums, Differences, and Combinations of Random Variables
Key Terms
In Summary Boxes
Exercises
Chapter 9: Understanding Sampling Distributions: Statistics as Random Variables
9.1: Parameters, Statistics,and Statistical Inference
9.2: From Curiosity to Questions about Parameters
9.3: SD Module 0: An Overview of Sampling Distributions
9.4: SD Module 1: Sampling Distribution for One Sample Proportion
9.5: SD Module 2: Sampling Distribution for the Difference in Two Sample Proportions
9.6: SD Module 3: Sampling Distribution for One Sample Mean
9.7: SD Module 4: Sampling Distribution for the Sample Mean of Paired Differences
9.8: SD Module 5: Sampling Distribution for the Difference in Two Sample Means
9.9: Preparing for Statistical Inference: Standardized Statistics
9.10: Generalizations beyond the Big Five
9.11: Finding the Pattern in Sample Means
Key Terms
In Summary Boxes
Exercises
Chapter 10: Estimating Proportions with Confidence
10.1: CI Module 0: An Overview of Confidence Intervals
10.2: CI Module 1: Confidence Interval for a Population Proportion
10.3: CI Module 2: Confidence Intervals for the Difference in Two Population Proportions
10.4: Using Confidence Intervals to Guide Decisions
Key Terms
In Summary Boxes
Exercises
Chapter 11: Estimating Means with Confidence
11.1: Introduction to Confidence Intervals for Means
11.2: CI Module 3: Confidence Intervals for One Population Mean
11.3: CI Module 4: Confidence Interval for the Population Mean of Paired Differences
11.4: CI Module 5: Confidence Interval for the Difference in Two Population Means (Independent Samples)
11.5: Understanding Any Confidence Interval
11.6: The Confidence Level in Action
Key Terms
In Summary Boxes
Exercises
Chapter 12: Testing Hypotheses about Proportions
12.1: HT Module 0: An Overview of Hypothesis Testing
12.2: HT Module 1: Testing Hypotheses about a Population Proportion
12.3: HT Module 2: Testing Hypotheses about the Difference in Two Population Proportions
12.4: Sample Size, Statistical Significance, and Practical Importance
Key Terms
In Summary Boxes
Exercises
Chapter 13: Testing Hypotheses about Means
13.1: Introduction to Hypothesis Tests for Means
13.2: HT Module 3: Testing Hypotheses about One Population Mean
13.3: HT Module 4: Testing Hypotheses about the Population Mean of Paired Differences
13.4: HT Module 5: Testing Hypotheses about the Difference in Two Population Means (Independent Samples)
13.5: The Relationship Between Significance Tests and Confidence Intervals
13.6: Choosing an Appropriate Inference Procedure
13.7: Effect Size
13.8: Evaluating Significance in Research Reports
Key Terms
In Summary Boxes
Exercises
Chapter 14: Inference about Simple Regression
14.1: Sample and Population Regression Models
14.2: Estimating the Standard Deviation for Regression
14.3: Inference about the Slope of a Linear Regression
14.4: Predicting y and Estimating Mean y at a Specific x
14.5: Checking Conditions for Using Regression Models for Inference
Key Terms
In Summary Boxes
Exercises
Chapter 15: More about Inference for Categorical Variables
15.1: The Chi-Square Test for Two-Way Tables
15.2: Analyzing 2X2 Tables
15.3: Testing Hypotheses about One Categorical Variable: Goodness of Fit
Key Terms
In Summary Boxes
Exercises
Chapter 16: Analysis of Variance
16.1: Comparing Means with an ANOVA F-Test
16.2: Details of One-Way Analysis of Variance
16.3: Other Methods for Comparing Populations
16.4: Two-Way Analysis of Variance
Key Terms
In Summary Boxes
Exercises
Chapter 17: Turning Information into Wisdom
17.1: Beyond the Data
17.2: Transforming Uncertainty into Wisdom
17.3: Making Personal Decisions
17.4: Control of Societal Risks
17.5: Understanding Our World
17.6: Getting to Know You
17.7: Words to the Wise
In Summary Boxes
Exercises
Appendix of Tables
References
Answers to Selected Odd-Numbered Exercises
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Text Credits
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
R
S
T
U
V
W
X
Y
Z
Case Studies and Examples
Chapter 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
Case Study 1.8 Did Your Mother’s Breakfast Determine Your Sex? 7
Chapter 2
Example 2.1 Seatbelt Use by Twelfth-Graders 21
Example 2.2 Lighting the Way to Nearsightedness 22
Example 2.3 Humans Are Not Good Randomizers 23
Example 2.4 Revisiting Example 2.2: Nightlight and Nearsightedness 24
Example 2.5 Right Handspans 26
Example 2.6 Annual Compensation for Highest Paid CEOs
in the United States 27
Example 2.7 Ages of Death of U.S. First Ladies 28
Example 2.8 Revisiting Example 2.7: Histograms for Ages of Death
of U.S. First Ladies 30
Example 2.9 Big Music Collections 32
Example 2.10 Median and Mean Quiz Scores 38
Example 2.11 Example 2.9 Revisited: Median and Mean Number
of Songs on Student iPods or MP3 Players 38
Example 2.12 Will “Normal” Rainfall Get Rid of Those Odors? 39
Example 2.13 Range and Interquartile Range for Fastest Speeds Ever
Driven 41
Example 2.14 Fastest Driving Speeds for Men 42
Example 2.15 Example 2.9 Revisited: Five-Number Summary
and Outlier Detection for Songs on iPod or MP3 Player 43
Example 2.16 Tiny Boatmen 46
Example 2.17 The Shape of British Women’s Heights 47
Example 2.18 Calculating a Standard Deviation 48
Example 2.19 Example 2.17 Revisited: Women’s Heights
and the Empirical Rule 50
Chapter 3
Example 3.1 Height and Handspan 70
Example 3.2 Driver Age and the Maximum Legibility Distance
of Highway Signs 71
Example 3.3 The Development of Musical Preferences 72
Example 3.4 Heights and Foot Lengths of College Women 73
Example 3.5 Describing Height and Handspan with a Regression Line 75
Example 3.6 Writing the Regression Equation for Height and Handspan 76
Example 3.7 Regression for Driver Age and the Maximum Legibility
Distance of Highway Signs 78
Example 3.8 Prediction Errors for the Highway Sign Data 80
Example 3.9 Calculating the Sum of Squared Errors 81
Example 3.10 The Correlation Between Handspan and Height 83
Example 3.11 The Correlation Between Age and Sign Legibility
Distance 83
Example 3.12 Left and Right Handspans 84
Example 3.13 Verbal SAT and GPA 84
Example 3.14 Age and Hours of Television Watching per Day 84
Example 3.15 Hours of Sleep and Hours of Study 85
Example 3.16 How Much Variability in Vision is Explained by Age? 86
Example 3.17 Height and Foot Length of College Women 90
Example 3.18 Earthquakes in the Continental United States 90
Example 3.19 Does It Make Sense? Height and Lead Feet 92
Example 3.20 Does It Make Sense? U.S. Population Predictions 93
Case Study 3.1 A Weighty Issue 97
Chapter 4
Example 4.1 Age and Main News Source 115
Example 4.2 Smoking and Divorce 116
Example 4.3 Sex and Rating of Quality of Public Education 117
Example 4.4 Sex and the Risk of Childhood Asthma 119
Example 4.5 Example 4.4 Revisited: Odds Ratio for Sex and Childhood
Asthma 120
Example 4.6 The Risk of a Shark Attack 121
Example 4.7 Case Study 1.2 Revisited: Disaster in the Skies? 121
Example 4.8 Dietary Fat and Breast Cancer 121
Case Study 4.1 Is Smoking More Dangerous for Women? 122
Example 4.9 Example 2.2 Revisited: Sleep-Time Lighting, Child Vision,
and Parents’ Vision 123
Example 4.10 U.S. Unemployment in 2009 and 1982 123
Example 4.11 Blood Pressure and Oral Contraceptive Use 124
Example 4.12 Case Study 1.6 Revisited: Aspirin and the Risk
of a Heart Attack 125
Example 4.13 Sex and Opinion about Banning Cell Phone Use while
Driving 127
Example 4.14 Example 4.13 Revisited: Expected Counts and Chi-Square
Statistic for Sex and Opinion about Banning Cell Phone Use while
Driving 129
Example 4.15 Breast Cancer Risk Stops Hormone Replacement Therapy
Study 130
Example 4.16 Case Study 1.6 Revisited: Aspirin and Heart Attacks 131
Case Study 4.2 Drinking, Driving, and the Supreme Court 133
Chapter 5
Example 5.1 Do First Ladies Represent Other Women? 148
Example 5.2 Do Penn State Students Represent Other College
Students? 148
Example 5.3 The Importance of Religion for Adult Americans 152
Example 5.4 Do You Want to Fly to the Moon? 152
Example 5.5 Choosing a Random Sample of Colleges
in the United States 156
Example 5.6 Representing the Heights of British Women 156
Example 5.7 An ABC News Poll on Parental Permissiveness 161
Example 5.8 The Current Population Survey 161
Example 5.9 Which Scientists Trashed the Public? 164
Example 5.10 A Meaningless Poll 165
Example 5.11 Haphazard Sampling 166
Case Study 5.1 The Infamous Literary Digest Poll of 1936 166
Example 5.12 Laid Off or Fired? 168
Example 5.13 Most Voters Don’t Lie, but Some Liars Don’t Vote 168
Example 5.14 Why Weren’t You at Work Last Week? 169
Example 5.15 Is Happiness Related to Dating? 169
Example 5.16 When Will Adolescent Males Report Risky Behavior? 169
Example 5.17 Politics Is All in the Wording 170
Example 5.18 Teenage Sex 171
Example 5.19 The Unemployed 171
Case Study 5.2 No Opinion of Your Own? Let Politics Decide 173
Chapter 6
Example 6.1 Case Study 1.5 Revisited: What Confounding Variables Lurk
behind Lower Blood Pressure? 191
Example 6.2 The Fewer the Pages, the More Valuable the Book? 192
Case Study 6.1 Lead Exposure and Bad Teeth 193
Case Study 6.2 Kids and Weight Lifting 195
Example 6.3 Revisiting Case Study 6.2: Randomly Assigning Children
to Weight-Lifting Groups 197
Case Study 6.3 Quitting Smoking with Nicotine Patches 199
Example 6.4 Blocked Experiment for Comparing Memorization
Methods 200
Case Study 6.4 Baldness and Heart Attacks 203
Example 6.5 Will Preventing Artery Clog Prevent Memory Loss? 206
Example 6.6 Dull Rats 208
Example 6.7 Real Smokers with a Desire to Quit 208
Example 6.8 Do Left-Handers Die Young? 209
Chapter 7
Case Study 7.1 A Hypothetical Story: Alicia Has a Bad Day 220
Example 7.1 Probability of Male versus Female Births 222
Example 7.2 A Simple Lottery 223
Example 7.3 The Probability That Alicia Has to Answer a Question 223
Example 7.4 The Probability of Lost Luggage 223
Example 7.5 Night-lights and Myopia Revisited 224
Example 7.6 Days per Week of Drinking Alcohol 226
Example 7.7 Probabilities for Some Lottery Events 227
Example 7.8 The Probability of Not Winning the Lottery 228
Example 7.9 Mutually Exclusive Events for Lottery Numbers 228
Example 7.10 Winning a Free Lunch 229
Example 7.11 The Probability That Alicia Has to Answer a Question 229
Example 7.12 Probability That a Teenager Gambles Differs for Boys
and Girls 230
Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Example 7.13 Probability a Stranger Does Not Share Your Birth Date 231
Example 7.14 Roommate Compatibility 231
Example 7.15 Probability of Either Two Boys or Two Girls in Two Births 232
Example 7.16 Probability That a Randomly Selected Ninth-Grader
Is a Male and a Weekly Gambler 233
Example 9.6 Men, Women, and the Death Penalty 330
Example 9.7 Hypothetical Mean Weight Loss 331
Example 9.8 Hypothetical Mean Weight Loss Revisited 333
Example 9.9 Suppose That There Is No “Freshman 15” 337
Example 9.10 Case Study 1.1 Revisited: Who Are the Speed
Example 7.17 Probability That Two Strangers Both Share Your Birth
Demons? 339
Month 233
Example 7.18 Probability That Alicia Is Picked for the First Question
Given That She Is Picked to Answer a Question 234
Example 7.19 The Probability of Guilt and Innocence Given a DNA
Match 235
Example 7.20 Choosing Left-Handed Students 236
Example 7.21 Winning the Lottery 238
Example 7.22 Prizes in Cereal Boxes 238
Example 7.23 Will Shaun’s Friends Be There for Him? 239
Example 7.24 Optimism for Alicia—She Is Probably Healthy 239
Example 7.25 Two-Way Table for Teens and Gambling 240
Example 7.26 Alicia’s Possible Fates 241
Example 7.27 The Probability That Alicia Has a Positive Test 242
Example 7.28 Tree Diagram for Teens and Gambling 242
Example 7.29 Getting All the Prizes 243
Example 7.30 Finding Gifted ESP Participants 244
Example 7.31 Two George D. Brysons 247
Example 7.32 Identical Cars and Matching Keys 247
Example 7.33 Winning the Lottery Twice 248
Example 7.34 Sharing the Same Birthday 248
Example 7.35 Unusual Hands in Card Games 249
Case Study 7.2 Doin’ the iPod Shuffle 251
Chapter 8
Example 8.1 Random Variables at an Outdoor Graduation or Wedding 264
Example 8.2 It’s Possible to Toss Forever 264
Example 8.3 Probability an Event Occurs Three Times in Three Tries 265
Example 8.4 Waiting on Standby 265
Example 8.5 Probability Distribution Function for Number of Courses 267
Example 8.6 Probability Distribution Function for Number of Girls 267
Example 8.7 Example 8.6 Revisited: Graph of pdf for Number of Girls 268
Example 8.8 Example 8.6 Revisited: Cumulative Distribution
for the Number of Girls 269
Example 8.9 Example 8.6 Revisited: A Mixture of Children 269
Example 8.10 Probabilities for Sum of Two Dice 270
Example 8.11 Gambling Losses 271
Example 8.12 California Decco Lottery Game 272
Example 8.13 Stability or Excitement—Same Mean, Different Standard
Deviations 273
Example 8.14 Mean Hours of Study for the Class Yesterday 274
Example 8.15 Probability of Two Wins in Three Plays 277
Example 8.16 Excel Calculations for Number of Girls in Ten Births 278
Example 8.17 Guessing Your Way to a Passing Score 278
Example 8.18 Is There Extraterrestrial Life? 280
Case Study 8.1 Does Caffeine Enhance the Taste of Cola? 280
Example 8.19 Time Spent Waiting for the Bus 282
Example 8.20 Example 8.19 Revisited: Probability That the Waiting Time
Is 5 to 7 Minutes 282
Example 8.21 College Women’s Heights 284
Example 8.22 Probabilities for Math SAT Scores 286
Example 8.23 Example 8.21 Revisited: z-Score for a Height of 62 Inches 288
Example 8.24 Example 8.21 Revisited: Probability That Height Is Less
Than 62 Inches 289
Example 8.25 Example 8.22 Revisited: Using Table A.1 to Find
Probabilities for Math SAT Scores 289
Example 8.26 The 75th Percentile of Systolic Blood Pressures 291
Example 8.27 The Number of Heads in 60 Flips of a Coin 292
Example 8.28 Normal Approximation to Binomial Distribution with
n 5 300 and p 5 .3 293
Example 8.29 Political Woes 294
Example 8.30 Guessing and Passing a True−False Test 294
Example 8.31 Will Meg Miss Her Flight? 298
Example 8.32 Can Alison Ever Win? 299
Example 8.33 Donations Add Up 300
Example 8.34 Strategies for Studying When You Are Out of Time 300
Chapter 9
Example 9.1 The “Freshman 15” 316
Example 9.2 Mean Hours of Sleep for College Students 321
Example 9.3 Scratch and Win (or Lose) Lotteries 325
Example 9.4 Possible Sample Proportions Favoring a Candidate 326
Example 9.5 Caffeinated or Not? 327
Example 9.11 Unpopular TV Shows 341
Example 9.12 Standardized Mean Weights 343
Example 9.13 The Long Run for the Decco Lottery Game 345
Example 9.14 California Decco Losses 346
Example 9.15 Winning the Lottery by Betting on Birthdays 347
Example 9.16 Constructing a Simple Sampling Distribution for the Mean
Movie Rating 349
Case Study 9.1 Do Americans Really Vote When They Say They Do? 352
Chapter 10
Example 10.1 Case Study 1.3 Revisited: Teens and Interracial Dating 373
Example 10.2 The Pollen Count Must Be High Today 375
Example 10.3 Is There Intelligent Life on Other Planets? 378
Example 10.4 Would You Return a Lost Wallet? 380
Example 10.5 Example 10.3 Revisited: 50% Confidence Interval for
Proportion Believing That Intelligent Life Exists Elsewhere 382
Example 10.6 Winning the Lottery and Quitting Work 383
Example 10.7 The Gallup Poll Margin of Error for n 5 1000 384
Example 10.8 Example 10.2 Revisited: Allergies and Really Bad
Allergies 385
Example 10.9 Age and Using the Internet as a News Source 387
Example 10.10 Do You Always Buckle Up When Driving? 388
Example 10.11 Which Drink Tastes Better? 390
Case Study 10.1 Extrasensory Perception Works with Movies 390
Case Study 10.2 Nicotine Patches versus Zyban® 391
Case Study 10.3 What a Great Personality 392
Chapter 11
Example 11.1 Pet Ownership and Stress 408
Example 11.2 Mean Hours per Day That Penn State Students Watch TV 409
Example 11.3 Do Men Lose More Weight by Diet or by Exercise? 410
Example 11.4 Finding the t* Values for 24 Degrees of Freedom
and 95% or 99% Confidence Intervals 412
Example 11.5 Are Your Sleeves Too Short? The Mean Forearm Length
of Men 414
Example 11.6 How Much TV Do Penn State Students Watch? 415
Example 11.7 What Type of Students Sleep More? 417
Example 11.8 Approximate 95% Confidence Interval for TV Time 420
Example 11.9 Screen Time—Computer versus TV 422
Example 11.10 Meditation and Anxiety 424
Example 11.11 The Effect of a Stare on Driving Behavior 427
Example 11.12 Parental Alcohol Problems and Child Hangover
Symptoms 428
Example 11.13 Confidence Interval for Difference in Mean Weight Losses
by Diet or Exercise 430
Example 11.14 Pooled t-Interval for Difference Between Mean Female
and Male Sleep Times 431
Example 11.15 Sleep Time with and without the Equal Variance
Assumption 433
Case Study 11.1 Confidence Interval for Relative Risk: Case Study 6.4
Revisited 435
Case Study 11.2 Premenstrual Syndrome? Try Calcium 435
Chapter 12
Example 12.1 Does a Majority Favor a Lower Limit for Drunk
Driving? 451
Example 12.2 Are Side Effects Experienced by Fewer Than 20%
of Patients? 453
Example 12.3 Mean Normal Body Temperature for Men and Women 453
Example 12.4 Stop the Pain Before It Starts 455
Example 12.5 Example 12.4 Revisited: p-Value for Comparing
the Painkiller and Control Groups 456
Example 12.6 A Jury Trial 457
Example 12.7 Errors in Medical Tests 459
Example 12.8 Calcium and the Relief of Premenstrual Symptoms 460
Example 12.9 Medical Tests Revisited 460
Example 12.10 Example 12.1 Revisited: Does a Majority Favor a Lower
BAC Limit for Drivers? 463
Example 12.11 The Importance of Order in Voting 466
Example 12.12 Example 12.2 Revisited: Do Fewer Than 20% Experience
Medication Side Effects? 467
Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Example 12.13 A Two-Sided Test: If Your Feet Don’t Match, Is the Right
One More Likely to Be Longer or Shorter? 469
Example 12.14 Case Study 10.1 Revisited: A Test for Extrasensory
Perception 470
Example 12.15 What Do Men Care About in a Date? 472
Example 12.16 Example 12.11 Revisited: Rejecting the Hypothesis
of Equal Choices 474
Example 12.17 The Prevention of Ear Infections 476
Example 12.18 How the Same Sample Proportion Can Produce Different
Conclusions 481
Example 12.19 Birth Month and Height 483
Example 12.20 Case Study 1.7 Revisited: The Internet and Loneliness 483
Example 12.21 Power and Sample Size for a Survey of Students 484
Case Study 12.1 An Interpretation of a p-Value Not Fit to Print 486
Chapter 13
Example 13.1 Normal Body Temperature for Young Adults 501, 503, 507
Example 13.2 Why Can’t the Pilot Have a Drink? 508
Example 13.3 Do You Know How Tall You Really Are? 511
Example 13.4 The Effect of a Stare on Driving Behavior 512, 514
Example 13.5 A Two-Tailed Test of Television Watching for Men
and Women 515
Example 13.6 Misleading Pooled t-Test for Television Watching for Men
and Women 519
Example 13.7 Legitimate Pooled t-Test for Comparing Male and Female
Sleep Time 519
Example 13.8 Mean Daily Television Hours of Men and Women 521
Example 13.9 Ear Infections and Xylitol 522
Example 13.10 Kids and Weight Lifting 525
Example 13.11 Loss of Cognitive Functioning 525
Example 13.12 Could Aliens Tell That Women Are Shorter? 527
Example 13.13 Normal Body Temperature 528
Example 13.14 The Hypothesis-Testing Paradox 528
Example 13.15 Planning a Weight-Loss Study 529
Case Study 13.1 Beat the Heat with a Frozen Treat 532
Chapter 14
Example 14.1 Residuals in the Handspan and Height Regression 551
Example 14.2 Mean and Deviation for Height and Handspan
Regression 553
Example 14.3 Relationship between Height and Weight for College
Men 555
Example 14.4 R 2 for Heights and Weights of College Men 557
Example 14.5 Driver Age and Highway Sign-Reading Distance 557
Example 14.6 Hypothesis Test for Driver Age and Sign-Reading
Distance 559
Example 14.7 95% Confidence Interval for Slope between Age
and Sign-Reading Distance 560
Example 14.8 Is Pulse Rate Related to Weight? 561
Example 14.9 Predicting When Someone Can Read a Sign 562
Example 14.10 Estimating Mean Weight of College Men at Various
Heights 565
Example 14.11 Checking Conditions 1 to 3 for the Weight and Height
Problem 567
Example 14.12 Chug-Time and Weight 569
Case Study 14.1 A Contested Election 571
Chapter 15
Example 15.1 Ear Infections and Xylitol Sweetener 584
Example 15.2 With Whom Do You Find It Easiest to Make Friends? 585
Example 15.3 Calculation of Expected Counts and Chi-Square
for the Xylitol and Ear Infection Data 587
Example 15.4 p-Value Area for the Xylitol Example 589
Example 15.5 Using Table A.5 for the Xylitol and Ear Infection Problem 590
Example 15.6 A Moderate p-Value 590
Example 15.7 A Tiny p-Value 590
Example 15.8 Making Friends 591
Example 15.9 Sex of Driver and Drinking before Driving 593
Example 15.10 Age and Tension Headaches 594
Example 15.11 Sheep, Goats, and ESP 595
Example 15.12 Butterfly Ballots 596
Example 15.13 Sex of Student and Car Accidents 598
Example 15.14 Asthma Prevalence over Time 600
Example 15.15 The Pennsylvania Daily Number 602
Case Study 15.1 Do You Mind If I Eat the Blue Ones? 604
Chapter 16
Example 16.1 Classroom Seat Location and Grade Point Average 618
Example 16.2 Application of Notation to the GPA and Classroom Seat
Sample 619
Example 16.3 Assessing the Necessary Conditions for the GPA and Seat
Location Data 621
Example 16.4 Occupational Choice and Testosterone Level 621
Example 16.5 The p-Value for the Testosterone and Occupational Choice
Example 622
Example 16.6 Pairwise Comparisons of GPAs Based on Seat Locations 624
Example 16.7 Comparison of Weight-Loss Programs 626
Example 16.8 Analysis of Variation among Weight Losses 628
Example 16.9 Top Speeds of Supercars 629
Example 16.10 95% Confidence Intervals for Mean Car Speeds 630
Example 16.11 Drinks per Week and Seat Location 631
Example 16.12 Kruskal–Wallis Test for Alcoholic Beverages per Week
by Seat Location 633
Example 16.13 Mood’s Median Test for the Alcoholic Beverages
and Seat Location Example 634
Example 16.14 Happy Faces and Restaurant Tips 636
Example 16.15 You’ve Got to Have Heart 637
Example 16.16 Two-Way Analysis of Variance for Happy Face Example 638
Chapter 17
Example 17.1 Playing the Lottery 654
Example 17.2 Surgery or Uncertainty? 655
Example 17.3 Fish Oil and Psychiatric Disorders 655
Example 17.4 Go, Granny, Go or Stop, Granny, Stop? 657
Example 17.5 When Smokers Butt Out Does Society Benefit? 658
Example 17.6 Is It Wining or Dining That Helps French Hearts? 659
Example 17.7 Give Her the Car Keys 660
Example 17.8 Lifestyle Statistics from the Census Bureau 661
Example 17.9 In Whom Do We Trust? 662
Supplemental Topic 1
Example S1.1 Random Security Screening
Example S1.2 Betting Birthdays for the Lottery
Example S1.3 Customers Entering a Small Shop
Example S1.4 Earthquakes in the Coming Year
Example S1.5 Emergency Calls to a Small Town Police Department
Example S1.6 Are There Illegal Drugs in the Next 5000 Cars?
Example S1.7 Calling On the Back of the Class
Supplemental Topic 2
Example S2.1 Normal Human Body Temperature
Example S2.2 Heights of Male Students and Their Fathers
Example S2.3 Estimating the Size of Canada’s Population
Example S2.4 Calculating T + for a Sample of Systolic Blood Pressures
Example S2.5 Difference Between Student Height and Mother’s Height
for College Women
Example S2.6 Comparing the Quality of Wine Produced in Three
Different Regions
Supplemental Topic 3
Example S3.1 Predicting Average August Temperature
Example S3.2 Blood Pressure of Peruvian Indians
Supplemental Topic 4
Example S4.1 Sleep Hours Based on Sex and Seat Location
Example S4.2 Pulse Rates, Sex, and Smoking
Example S4.3 Nature Versus Nurture in IQ Scores
Example S4.4 Happy Faces and Restaurant Tips Revisited
Example S4.5 Does Smoking Lead to More Errors?
Supplemental Topic 5
Example S5.1 Stanley Milgram’s “Obedience and Individual
Responsibility” Experiment
Example S5.2 Janet’s (Hypothetical) Dissertation Research
Example S5.3 Jake’s (Hypothetical) Fishing Expedition
Example S5.4 The Debate Over Passive Smoking
Example S5.5 Helpful and Harmless Outcomes from Hormone
Replacement Therapy
Case Study S5.1 Science Fair Project or Fair Science Project?
Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Mind on Statistics
Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Mind on Statistics
Fourth Edition
Jessica M. Utts
University of California, Irvine
Robert F. Heckard
Pennsylvania State University
Australia • Brazil • Japan • Korea • Mexico • Singapore • Spain • United Kingdom • United States
Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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