R × Python

Online textbook companion

Regression
with R & Python

Description, Prediction, and Causal Analysis for Applied Social Science and Econometrics

Per Johansson and Jiajing Sun

Browse all chaptersOpen code & QR →
One organising principle

Start with the research question. Then choose the design, model, assumptions, and code.

Contents

Fourteen chapters

Every chapter uses the same reading structure: guide, concepts, methods and formulas, empirical case, diagnostics, extensions, and exercises with code.

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

Authors

Two perspectives.
One practical approach.

The book combines statistical methodology, econometrics, policy evaluation, finance, and applied research with a shared emphasis on interpretation and reproducible practice.

Portrait of Per Johansson

Statistics · Causal inference · Policy evaluation

Per Johansson

Chair Professor of Statistics at Uppsala University and Professor at the Yau Mathematical Sciences Center, Tsinghua University. His research connects causal inference and program evaluation with labour economics, social insurance, health economics, and evidence-based policy.

Emailper.johansson@statistik.uu.se
  • Uppsala University
  • Tsinghua University
  • IZA Research Fellow
Portrait of Jiajing Sun

Econometrics · Statistics · Finance

Jiajing Sun

Associate Professor at the School of Economics and Management, University of Chinese Academy of Sciences, and Deputy Director of its Department of Statistics and Data Science. Her research spans econometrics, statistics, finance, and robust inference.

Emailjiajing@ucas.ac.cn
  • UCAS
  • CFA
  • FIMA

Computational companion

Read the method.
Run the example.

Chapter-organised R and Python scripts, runnable workbooks, datasets, and printed-book QR targets.

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