R × Python

Contents

Fourteen chapters

Every chapter uses the same reading structure: guide, concepts, methods and formulas, empirical case, diagnostics, extensions, and exercises with code. Use the sequence as a course roadmap, or enter at the chapter that matches your empirical question.

I

Questions and foundations

  1. IntroductionQuestions, design, and data structures
  2. Covariation in dataPlots, correlation, regression, and R²
  3. Probability and inferenceEstimators, CLT, tests, and intervals
  4. Population correlationSampling, Fisher transformation, dependence
II

Regression models

  1. Simple linear regressionOLS, interpretation, robust inference
  2. Multiple linear regressionPartial effects, FWL, omitted variables
  3. Nonlinear functional formPolynomials, logs, and interactions
  4. Dependent error termsClusters, multilevel models, panels
  5. Binary dependent variablesLogit, risks, likelihood
III

Prediction and flexible methods

  1. PredictionValidation, regularisation, and trees
  2. Nonparametric regressionKernels, bandwidths, and RDD
  3. Time series analysisStationarity, dynamics, forecasting
IV

Causal design and synthesis

  1. Causal analysisPotential outcomes and research designs
  2. Key concepts and synthesisAssumptions, workflow, reproducibility
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