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Author:   Nathan Tintle (California Polytechnic State University, San Luis Obispo) ,  Beth L. Chance (California Polytechnic State University, San Luis Obispo) ,  Karen McGaughey ,  Soma Roy
Publisher:   John Wiley & Sons Inc
Imprint:   John Wiley & Sons Inc
Dimensions:   Width: 21.10cm , Height: 2.50cm , Length: 26.90cm
Weight:   1.179kg
ISBN:  

9781119634522

ISBN 10:   1119634520
Pages:   608
Publication Date:   09 September 2020
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Loose-leaf
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

Preliminaries 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

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