Description
Book SynopsisTable of ContentsPreliminaries Multivariable Thinking and Sources of Variation 1
Example P.A: Graduate School Admissions at Berkeley 2
Exploration P.A: Salary Discrimination 9
Example P.B: Predicting Birth Weights 15
Exploration P.B: Housing Prices in Michigan 21
1 Sources of Variation 31
Section 1.1: Sources of Variation in an Experiment 32
Example 1.1: Scents and Consumer Behavior 33
Exploration 1.1: Memorizing Letters 40
Section 1.2: Quantifying Sources of Variation 44
Example 1.2: Scents and Consumer Behavior cont. 44
Exploration 1.2: Starry Navigation 50
Section 1.3: Is the Variation Explained Statistically Significant? 56
Example 1.3: Scents and Consumer Behavior cont. 57
Exploration 1.3: Starry Navigation cont. 65
Section 1.4: Comparing Several Groups 71
Example 1.4: Fish Consumption and Omega-3 72
Exploration 1.4: Golden Squirrels 83
Section 1.5: Confidence and Prediction Intervals 88
Example 1.5: Fish Consumption and Omega-3 cont. 89
Exploration 1.5: Golden Squirrels cont. 97
Section 1.6: More Study Design Considerations 101
Example 1.6: Fish Consumption and Omega-3 (revisited) 101
Exploration 1.6: Who Is Spending More Time Parenting on Average? 109
2 Controlling Additional Sources of Variation 138
Section 2.1: Paired Data 139
Example 2.1: Texts vs. Visual Distractions (Facebook vs. Instagram) 140
Exploration 2.1: Chip Melting Times 148
Section 2.2: Randomized Complete Block Designs 152
Example 2.2: What’s All the Fuss about Caffeine? 152
Exploration 2.2: Strawberry Storage 164
Section 2.3: Observational Studies with Two Explanatory Variables 173
Example 2.3: Salary Discrimination cont. 174
Exploration 2.3: Car Acceleration 182
3 Multi-factor Studies and Interactions 210
Section 3.1: Multi-factor Experiments 211
Example 3.1: Corporate Credibility, Endorser, and Purchase Intent 212
Exploration 3.1: Pig Growth 222
Section 3.2: Statistical Interactions 228
Example 3.2: Pistachio Bleaching 228
Exploration 3.2: Optimizing Ads 239
Section 3.3: Replication 248
Example 3.3: Optimizing Vitamin C 248
Exploration 3.3: Hurricane Names 257
Section 3.4: Interactions in Observational Studies 262
Example 3.4: Salary Discrimination revisited 262
Exploration 3.4: Hopelessness and Exercise 267
4 Including a Quantitative Explanatory Variable 294
Section 4.1: Quantitative Explanatory Variables 295
Example 4.1: Recovering Polyphenols from Grape Seed 295
Exploration 4.1: Fatty Acids and DNA 304
Section 4.2: Inference for Simple Linear Regression 308
Example 4.2: Recovering Polyphenols from Grape Seed cont. 309
Exploration 4.2: Fatty Acids and DNA cont. 317
Section 4.3: Quantitative and Categorical Explanatory Variables 322
Example 4.3: Michigan Housing Prices 323
Exploration 4.3: Predicting Height 332
Section 4.4: Quantitative/Categorical Interactions 338
Example 4.4: Michigan Housing Prices cont. 338
Exploration 4.4: FEV and Smoking 344
Section 4.5: Multi-level Categorical Variables 348
Example 4.5: Diamonds 348
Exploration 4.5: Patient Satisfaction 358
5 Multiple Quantitative Explanatory Variables 383
Section 5.1: Experiments with Multiple Quantitative Explanatory Variables 384
Example 5.1: Pistachio Bleaching 384
Exploration 5.1: Biodiesel 397
Section 5.2: Observational Studies with Multiple Quantitative Explanatory Variables 403
Example 5.2: Brain Size and IQ 403
Exploration 5.2: SLO Real Estate Data 410
Section 5.3: Modeling Nonlinear Associations Part I—Polynomial Models 414
Example 5.3: Arctic Sea Ice 414
Exploration 5.3: Kentucky Derby Winning Times 419
Section 5.4: Modeling Nonlinear Associations Part II—Transformations 421
Example 5.4: Salary Discrimination cont. 422
Exploration 5.4A: Stopping Distances 424
Exploration 5.4B: Kentucky Derby Winning Times cont. 426
6 Categorical Response Variable 447
Section 6.1: Comparing Proportions 448
Example 6.1: Encouraging Organ Donation 448
Exploration 6.1: Infant Attachment 460
Section 6.2: Introduction to Logistic Regression 465
Example 6.2: Smoking and Survival Rates 466
Exploration 6.2: Alcohol Abuse in Ukraine 472
Section 6.3: Multiple Logistic Regression Models 476
Example 6.3: Smoking and Survival Rates cont. 477
Exploration 6.3: Alcohol Abuse in Ukraine cont. 483
7 Practical Issues 503
Section 7.1: Dealing with the Messes Created by Messy Data 504
Example 7.1: Public Health Screening Data for the Omega-3 Index 504
Exploration 7.1: Evaluating the Impact of a Water Filter Intervention 516
Section 7.2: Multiple Regression with Many Explanatory Variables 524
Example 7.2: Predicting Real Estate Prices 524
Exploration 7.2: Predicting Changes in Omega-3 Index Values 536
Solutions to Selected Exercises 543
Index 579
