Description
TABLE OF CONTENTS
Preliminaries Introduction to Statistical Investigations 1
Section P.1: Introduction to the Six-Step Method 2
Example P.1: Organ Donations 2
Section P.2: Exploring Data 7
Example P.2: Oh, Say Can You Sing? 7
Section P.3: Exploring Random Processes 14
Exploration P.3: Cars or Goats 14
Unit 1 Four Pillars of Inference: Strength, Size, Breadth, and Cause 30
1Â Significance: How Strong Is the Evidence? 31
Section 1.1: Introduction to Chance Models 32
Example 1.1: Can Dolphins Communicate? 33
Exploration 1.1: Can Dogs Understand Human Cues? 41
Section 1.2: Measuring the Strength of Evidence 45
Example 1.2: Rock-Paper-Scissors 46
Exploration 1.2: Tasting Water 52
Section 1.3: Alternative Measure of Strength of Evidence 57
Example 1.3: Heart Transplant Operations 58
Exploration 1.3: Do People Use Facial Prototyping? 62
Section 1.4: What Impacts Strength of Evidence? 66
Example 1.4: Predicting Elections from Faces? 66
Exploration 1.4: Competitive Advantage to Uniform Colors? 72
Section 1.5: Inference for a Single Proportion: Theory-Based Approach 75
Example 1.5: Halloween Treats 77
Exploration 1.5: Eye Dominance 80
2Â Generalization: How Broadly Do the Results Apply? 117
Section 2.1: Sampling from a Finite Population: Proportions 118
Example 2.1: Voter Turnout 119
Exploration 2.1: Sampling Words 126
Section 2.2: Quantitative Data 133
Example 2.2: Sampling Students 134
Exploration 2.2: Sampling Words (cont.) 138
Section 2.3: Theory-based Inference for a Population Mean 143
Example 2.3: Estimating Elapsed Time 143
Exploration 2.3: Sleepless Nights? 150
Section 2.4: Other Statistics 154
Example 2.4: Estimating Elapsed Time (cont.) 154
Exploration 2.4: Backpack Weights 160
3Â Estimation: How Large Is the Effect? 187
Section 3.1: Statistical Inference: Confidence Intervals 188
Example 3.1: Can Dogs Sniff Out Cancer? 189
Exploration 3.1: Kissing Right? 194
Section 3.2: 2SD and Theory-Based Confidence Intervals for a Single Proportion 198
Example 3.2: Cyberbullying 198
Exploration 3.2: How Mobile Are We? 203
Section 3.3: 2SD and Theory-Based Confidence Intervals for a Single Mean 207
Example 3.3: Used Cars 207
Exploration 3.3: Sleepless Nights? (cont.) 210
Section 3.4: Factors That Affect the Width of a Confidence Interval 213
Example 3.4: American Cat Ownership 214
Exploration 3.4A: Holiday Spending Habits 216
Exploration 3.4B: Reese’s Pieces 218
4Â Causation: Can We Say What Caused the Effect? 245
Section 4.1: Association and Confounding 246
Example 4.1: Night Lights and Nearsightedness 247
Exploration 4.1: Home Court Disadvantage? 250
Section 4.2: Observational Studies Versus Experiments 252
Example 4.2: Lying on the Internet 253
Exploration 4.2: Have a Nice Trip 257
Unit 2 Comparing Two Groups 278
5Â Comparing Two Proportions 279
Section 5.1: Comparing Two Groups: Categorical Response 280
Example 5.1: Buckling Up? 280
Exploration 5.1: Murderous Nurse? 285
Section 5.2: Comparing Two Proportions: Simulation-Based Approach 288
Example 5.2: Swimming with Dolphins 289
Exploration 5.2: Is Yawning Contagious? 297
Section 5.3: Comparing Two Proportions: Theory-Based Approach 304
Example 5.3: Parents’ Smoking Status and Their Babies’ Sex 305
Exploration 5.3: Donating Blood 311
6Â Comparing Two Means 346
Section 6.1: Comparing Two Groups: Quantitative Response 347
Example 6.1: Geyser Eruptions 347
Exploration 6.1: Cancer Pamphlets 350
Section 6.2: Comparing Two Means: Simulation-Based Approach 354
Example 6.2: Dung Beetles 354
Exploration 6.2: Lingering Effects of Sleep Deprivation 363
Section 6.3: Comparing Two Means: Theory-Based Approach 369
Example 6.3: Violent Video Games and Aggression 369
Exploration 6.3: Close Friends 378
7Â Paired Data: One Quantitative Variable 407
Section 7.1: Paired Designs 408
Example 7.1: Can You Study with Music Blaring? 408
Exploration 7.1: Rounding First Base 411
Section 7.2: Simulation-Based Approach to Analyzing Paired Data 413
Example 7.2: Rounding First Base (cont.) 414
Exploration 7.2: Exercise and Heart Rate 420
Section 7.3: Theory-Based Approach to Analyzing Data from Paired Samples 425
Example 7.3: Dad Jokes? 425
Exploration 7.3: Comparing Auction Formats 431
Unit 3 Analyzing More General Situations 456
8Â Comparing More Than Two Proportions 458
Section 8.1: Comparing Multiple Proportions: Simulation-Based Approach 459
Example 8.1: Coming to a Stop 460
Exploration 8.1: Recruiting Organ Donors 466
Section 8.2: Comparing Multiple Proportions: Theory-Based Approach 470
Example 8.2: Sham Acupuncture 471
Exploration 8.2A: Conserving Hotel Towels 476
Exploration 8.2B: Nearsightedness and Night Lights Revisited 480
Section 8.3: Chi-Square Goodness-of-Fit Test 484
Example 8.3: Fair Die? 484
Exploration 8.3: Are Birthdays Equally Distributed Throughout the Week? 490
9Â Comparing More Than Two Means 519
Section 9.1: Comparing Multiple Means: Simulation- Based Approach 520
Example 9.1: Comprehending Ambiguous Prose 520
Exploration 9.1: Exercise and Brain Volume 525
Section 9.2: Comparing Multiple Means: Theory-Based
Approach 529
Example 9.2: Recalling Ambiguous Prose 530
Exploration 9.2: Comparing Popular Diets 538
10Â Two Quantitative Variables 565
Section 10.1: Two Quantitative Variables: Scatterplots and Correlation 566
Example 10.1: Why Whales Are Big, but Not Bigger 567
Exploration 10.1: Height and Winning at Tennis 571
Section 10.2: Inference for the Correlation Coefficient: Simulation-Based Approach 576
Example 10.2: Exercise Intensity and Mood Changes 576
Exploration 10.2: Draft Lottery 580
Section 10.3: Least Squares Regression 585
Example 10.3: Height and Winning at Tennis (cont.) 585
Exploration 10.3: Predicting Height from Footprints 590
Section 10.4: Inference for the Regression Slope: Simulation-Based Approach 596
Example 10.4: Do Students Who Spend More Time in Non-Academic Activities Tend to Have Lower GPAs? 596
Exploration 10.4: Predicting Brain Density from Number of Facebook Friends 599
Section 10.5: Inference for the Regression Slope: Theory-Based Approach 601
Example 10.5A: Predicting Heart Rate from Body Temperature 602
Example 10.5B: Smoking and Drinking 606
Exploration 10.5: Predicting Brain Density from Number of Facebook Friends (cont.) 608
Unit 4 Probability (Online) 11-1
11Â Modeling Randomness 11-2
Section 11.1: Basics of Probability 11-3
Example 11.1: Random Ice Cream Prices 11-3
Exploration 11.1: Random Babies 11-8
Section 11.2: Probability Rules 11-10
Example 11.2: Watching Films 11-11
Exploration 11.2: Random Ice Cream Prices (cont.) 11-15
Section 11.3: Conditional Probability and Independence 11-19
Example 11.3: Watching Films Revisited 11-20
Exploration 11.3A: College Admissions 11-25
Exploration 11.3B: Rare Disease Testing 11-28
Section 11.4: Discrete Random Variables 11-30
Example 11.4: A Game of Chance 11-30
Exploration 11.4: Traffic Lights 11-35
Section 11.5: Random Variable Rules 11-38
Example 11.5: A Game of Chance Revisited 11-38
Exploration 11.5: Skee-Ball 11-45
Section 11.6: Binomial and Geometric Random Variables 11-50
Example 11.6: Time to Leave the Nest? 11-52
Exploration 11.6: Clueless Quiz 11-59
Section 11.7: Continuous Random Variables and Normal Distributions 11-63
Example 11.7: Heights of Adult Women 11-65
Exploration 11.7A: Birthweights 11-69
Exploration 11.7B: Run, Girl, Run! 11-71
Section 11.8: Revisiting Theory-Based Approximations of Sampling Distributions 11-72
Example 11.8A: Time to Leave the Nest Revisited 11-74
Example 11.8B: Intelligence Test 11-75
Exploration 11.8A: Racket Spinning 11-77
Exploration 11.8B: Random Ice Cream Prices (cont.) 11-77
Appendix A Calculation Details 645
Appendix B Stratified and Cluster Samples 662
Solutions to Selected Exercises 666
Index 728
