Description
Summarise patterns and communicate how variables move together.
A practical econometrics companion
Regression
with R & Python
Description, Prediction, and Causal Analysis for Applied Social Science and Econometrics
14 chaptersR + Py parallel code09 learning modules
One framework, three questions
Start with the question.
Then choose the model.
The book builds a bridge from statistical reasoning to reproducible practice. It shows how the same regression toolkit changes when the goal is to describe a relationship, predict an outcome, or estimate a causal effect.
Prediction
Evaluate out-of-sample performance and manage model complexity.
Causal analysis
Connect assumptions, research design, and credible counterfactuals.
Learn by translating
The idea stays the same.
The syntax changes.
Examples are developed side by side so readers can focus on the statistical idea while becoming fluent in both ecosystems.
# Estimate a linear relationship
model <- lm(y ~ x, data = sample)
summary(model)
predict(model, newdata = future)# Estimate a linear relationship
model = smf.ols("y ~ x", data=sample).fit()
model.summary()
model.predict(future)Contents
A guided path through regression
Fourteen chapters move from foundations to flexible prediction and research designs for causal analysis. Companion code is linked where available.
I Foundations
- IntroductionQuestions before methods
- Covariation in dataRead lesson →
- Basic probability theory and statistical inferenceExplore code ↗
- Correlation and inference about a populationExplore code ↗
II Regression models
III Prediction & causality
- PredictionExplore code ↗
- Nonparametric regression methodsExplore code ↗
- Time series analysisExplore code ↗
- Causal analysesRead lesson →
- Key conceptsSynthesis and review
From the teaching slides
Three ideas,
rebuilt for the web.
Selected material from the authors’ slide deck is presented as concise, responsive lessons. The complete manuscript and slide archive remain off the site.
Open companion
Run the examples.
Make them your own.
The public repository contains chapter-organised R and Python scripts, runnable workbooks, and example datasets.
Public companion scope
This site intentionally does not reproduce the manuscript, slides, solutions, or publisher files. Availability of additional teaching materials is determined by the authors and publisher.