Preface 1 - Introduction 1.1 Book Overview 1.2 Chapter Summaries 1.3 How to Use This Book 1.4 Why Learn to Analyze Data? 1.5 Getting Ready 1.6 Introduction to R 1.7 Loading and Making Sense of Data 1.8 Computing and Interpreting Means 2 - Estimating Cusal Effects with Randomized Experiments 2.1 Project STAR 2.2 Treatment and Outcome Variables 2.3 Individual Causal Effects 2.4 Average Causal Effects 2.5 Do Small Classes Improve Student Perfomance? 3 - Inferring Population Characteristiics via Survey Research 3.1 The EU Referendum in the UK 3.2 Survey Research 3.3 Measuring Support for Brexit 3.4 Who Supported Brexit? 3.5 Relationship between Education and the Leave Vote in the Entire UK 4 - Predicting Outcomes Using Linear Regression 4.1 GDP and Night-Time Light Emissions 4.2 Predictions, Observed vs. Predicted Outcomes, and Prediction Errors 4.3 Summarizing the Relationship between Two Variables with a Line 4.4 Predicting GDP Using Prior GDP 4.5 Predicting GDP Growth Using Night-Time Light Emissions 4.6 Measuring How Well the Model Fits the Data with thCoefficient of Determination, R² 5 - Estimating Causal Effects with Observational Data 5.1 Russian State-Controlled TV Coverage of 2014 Ukrainian 5.2 Challenges of Estimating Causal Effects with Observational Data 5.3 The Effect of Russian TV on Ukrainians' Voting Behavior 5.4 The Effect of Russian TV Russian TV on Ukrainian Electoral Outcomes 5.5 Internal and External Validity 6 - Probability 6.1 What Is Probability? 6.2 Axioms of Probability 6.3 Events, Random Variables, and Probability Distributions 6.4 Probability Distributions 6.5 Population Parameters vs. Sample Statistics 7 - Quantifying Uncertainty 7.1 Estimators and Their Sampling Distributions 7.2 Confidence Intervals 7.4 Statistical vs. Scientific Significance Index of Concepts Index of Mathematical Notation Index of R and RStudio