Intermediate Statistical Investigations

by Tintle, Nathan; Chance, Beth L.; McGaughey, Karen; Roy, Soma; Swanson, Todd; VanderStoep, Jill

• ISBN13: 9781119634522
• ISBN10: 1119634520
• Edition: 1st
• Format: Loose-leaf
• Copyright: 2020-09-09
• Publisher: Wiley

Summary

Intermediate Statistical Investigations provides a unified framework for explaining variation across study designs and variable types, helping students increase their statistical literacy and appreciate the indispensable role of statistics in scientific research. Requiring only a single introductory statistics course as a prerequisite, the program uses the immersive, simulation-based inference approach for which the author team is known.Students engage with various aspects of data collection and analysis using real examples and clear explanations designed to strengthen multivariable understanding and reinforce first-course concepts. 

Each chapter contains in-depth exercises which follow a consistent  six-step statistical exploration and investigation method (ask a research question, design a study, explore the data, draw inferences, formulate conclusions, and look back and ahead) enabling students to assess a variety of concepts in a single assignment. Challenging questions based on research articles strengthen critical reading skills, fully worked examples demonstrate essential concepts and methods, and engaging visualizations illustrate key themes of explained variation. End-of-chapter investigations use real data from popular culture and published research studies in a variety of disciplines, exposing students to various applications of statistics in the real world. Throughout the text, user-friendly Rossman Chance web applets allow students to conduct the simulations and analyses covered in the book.

 

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