Online textbook companion
Choose a chapter.
Read in order.
Each chapter follows the same textbook structure: guide, core concepts, methods and formulas, an empirical case, diagnostics, extensions, and exercises with code.
Contents
Four parts.
One connected course.
Questions and foundations
Chapters 1–4
Introduction
Research questions, signal and noise, design, and data structures.
Read chapter → 02Covariation in data
Scatter plots, correlation, regression lines, residuals, and R².
Read chapter → 03Probability and inference
Random variables, estimators, the CLT, tests, and intervals.
Read chapter → 04Population correlation
Sampling uncertainty, Fisher transformation, and dependence.
Read chapter →Regression models
Chapters 5–9
Simple linear regression
Population models, OLS, interpretation, and robust inference.
Read chapter → 06Multiple regression
Partial effects, FWL, omitted variables, tests, and diagnostics.
Read chapter → 07Nonlinear form
Polynomials, categories, logarithms, and interactions.
Read chapter → 08Dependent errors
Clusters, multilevel models, panels, and fixed effects.
Read chapter → 09Binary outcomes
Logit, odds and risks, marginal effects, and likelihood.
Read chapter →Prediction and flexible methods
Chapters 10–12