Introduction 1 Book 1: Introducing R 5 Chapter 1: R: What It Does and How It Does It 7 Chapter 2: Working with Packages, Importing, and Exporting 37 Book 2: Describing Data 51 Chapter 1: Getting Graphic 53 Chapter 2: Finding Your Center 93 Chapter 3: Deviating from the Average 103 Chapter 4: Meeting Standards and Standings 113 Chapter 5: Summarizing It All 125 Chapter 6: What's Normal? 145 Book 3: Analyzing Data 163 Chapter 1: The Confidence Game: Estimation 165 Chapter 2: One-Sample Hypothesis Testing 181 Chapter 3: Two-Sample Hypothesis Testing 207 Chapter 4: Testing More than Two Samples 233 Chapter 5: More Complicated Testing 257 Chapter 6: Regression: Linear, Multiple, and the General Linear Model 279 Chapter 7: Correlation: The Rise and Fall of Relationships 315 Chapter 8: Curvilinear Regression: When Relationships Get Complicated 335 Chapter 9: In Due Time 359 Chapter 10: Non-Parametric Statistics 371 Chapter 11: Introducing Probability 393 Chapter 12: Probability Meets Regression: Logistic Regression 415 Book 4: Learning from Data 423 Chapter 1: Tools and Data for Machine Learning Projects 425 Chapter 2: Decisions, Decisions, Decisions 449 Chapter 3: Into the Forest, Randomly 467 Chapter 4: Support Your Local Vector 483 Chapter 5: K-Means Clustering 503 Chapter 6: Neural Networks 519 Chapter 7: Exploring Marketing 537 Chapter 8: From the City That Never Sleeps 557 Book 5: Harnessing R: Some Projects to Keep You Busy 573 Chapter 1: Working with a Browser 575 Chapter 2: Dashboards -- How Dashing! 603 Index 639.