Learning Track: Introduction to Regression in R

Claudius Gräbner-Radkowitsch

Learning Track: Introduction to Regression in R

regression

statistics

R

BA

A step-by-step learning path through linear regression for BA students — from the mathematics of logarithms through model fitting, diagnostics, and categorical predictors, entirely in R.

Author

Claudius Gräbner-Radkowitsch

Published

June 16, 2026

Modified

June 16, 2026

1 Who This Track Is For

This track is for BA students who have encountered the idea of regression in a statistics or methods course and now want to implement it in R. It assumes you can read a scatterplot, have some intuition about what a “line of best fit” does, and have worked through the basic R programming resources (or have equivalent experience).

By the end of this track you will be able to:

Fit and interpret a linear regression model in R using lm()
Apply log transformations to variables and interpret log-transformed coefficients correctly
Read standard regression diagnostic plots and identify common assumption violations
Extend a model with categorical predictors (dummy variables) and interaction terms

The track covers linear regression for BA-level students. For extensions into logistic regression, panel data, and causal reasoning, see the Regression and Causal Inference track (MA level).

2 Core Sequence

2.1 1. Logarithms

Logarithms: A Gentle Introduction

Why logarithms appear so often in regression models, how to work with them in R, and how to interpret coefficients when variables are log-transformed. A short conceptual page that pays dividends in every regression you run.

2.2 2. Linear Regression in R

Linear Regression in R

Fit a bivariate and multiple regression with lm(), interpret coefficients and R², understand what omitted variable bias means in practice, and apply log and quadratic transformations. The methodological core of this track.

2.3 3. Regression Diagnostics

Regression Residuals and Model Diagnostics

Learn what the four standard diagnostic plots are checking, how to spot problems (non-linearity, heteroskedasticity, outliers), and what to do about them. A model that has not been diagnosed is a model that should not be trusted.

2.4 4. Categorical Variables and Interactions

Categorical Variables and Interaction Terms in Regression

Extend your regression toolkit to handle binary and multi-category predictors: how R encodes them as dummies, how to choose a reference category, and how to specify and interpret interaction terms when the relationship between two variables depends on a third.

3 Related Resources

Title	Description
Common Probability Distributions: A Reference Overview	An overview of probability distributions beyond the normal: binomial, Poisson, uniform, exponential, beta, and gamma — with guidance on when to use each and how to work with them in R.
Descriptive Statistics in R	A guided introduction to descriptive statistics in R, covering measures of centrality, dispersion, and association — including correlation matrices, simple linear regression, and Anscombe’s quartet as a lesson in why visualization matters.
Hypothesis Testing and Confidence Intervals	Applying sampling theory to make inferences: how to evaluate claims using data (hypothesis testing and p-values) and how to construct ranges of plausible values for population parameters (confidence intervals).
Essentials of Probability Theory for Statistics	A conceptual and practical introduction to probability theory: random experiments, events, conditional probability, random variables, the normal distribution, expected values, and the bridge to descriptive statistics.
Sampling and Statistical Inference	How we move from sample statistics to conclusions about populations: sampling variability, Monte Carlo simulation, sampling distributions, standard error, the Central Limit Theorem, and the Law of Large Numbers.
Data Preparation with the Tidyverse	How to reshape data between wide and long format with tidyr, manipulate variables and filter rows with dplyr, merge data sets, and chain steps together using pipes.
Exercise: Causal Reasoning and the Gender Wage Gap	Practice DAG-based causal reasoning using a three-model regression progression on the gender wage gap — identify confounders and mediators, interpret coefficient changes, and specify a DAG in dagitty.
Exercise: Predicting Firm Exit with Logistic Regression	Practice logistic regression end-to-end: fit a model for binary firm exit, extend it with additional predictors, interpret odds ratios, compute scenario-based predicted probabilities, and communicate findings with inline R code.
Experiments in R	How to analyze experimental data in R: t-tests for simple comparisons, one-way and two-way ANOVA for multi-group designs, effect sizes, post-hoc tests, and power analysis.
First Steps in R	Learn the basic syntax of R: how to issue commands, use objects, assignments, and functions, and how to install and use R packages.
Getting Started with Quarto	An introduction to Quarto — the modern successor to R Markdown — covering how to create documents that mix text and code, control output rendering, and produce HTML and PDF reports.
Hypothesis Testing and Confidence Intervals: Practice Exercises	Conceptual and computational exercises to test and reinforce your understanding of hypothesis testing, p-values, confidence intervals, and the connections between them.
Importing and Exporting Data in R	How to read CSV files into R using data.table::fread(), handle common format variations (separators, decimal signs, leading zeros), and save data to CSV, RDS, RData, and Stata formats.
Getting Started with R: Installing Everything You Need	Step-by-step guide to installing R, RStudio, Git, Quarto, and registering for GitHub and Netlify so you have a complete working environment for data science.
Installing R Packages	How to install the R packages needed for data science work, verify your installation using the DataScienceExercises package, and set up LaTeX for PDF output.
Linear Regression in R	Practical implementation of simple and multiple linear regression in R: fitting models with lm(), interpreting coefficients, computing R², understanding omitted variable bias, applying log and quadratic transformations, and reading diagnostic plots.
Logarithms: A Gentle Introduction	A practical introduction to logarithms for data science: what they are, the natural logarithm, the relationship with exponential functions, and when and why to use log transformations in statistical analysis.
Logistic Regression for Binary Outcomes in R	Understand why OLS fails for binary outcomes, fit logistic regression with glm(), and translate log-odds into predicted probabilities and average marginal effects.
Categorical Variables and Interaction Terms in Regression	Extend linear regression to handle categorical predictors with multiple levels and interaction terms, using a real earnings dataset to explore how the age–earnings profile differs by gender.
Panel Data and Fixed Effects in R	Understand the logic of panel data, learn to distinguish within- from between-unit variation, estimate fixed effects models with fixest, and connect the FE identification argument to causal reasoning.
Publication-Ready Figures with ggplot2	Move beyond exploratory plots to publication-ready figures: choose the right chart type, apply the 7-item checklist, and use patchwork, ggrepel, gghighlight, ggtext, and scales to produce submission-quality output.
Object Types in R I: Functions	An introduction to functions as R objects: how to call them in prefix and infix form, how to work with arguments, and how to define and document your own functions.
Object Types in R II: Vectors	A thorough introduction to atomic vectors and lists in R: types (logical, integer, double, character), coercion, logical operations, vectorization, indexing, and useful functions.
Object Types in R III: Factors and Data Frames	An introduction to factors and data frames in R: how they are built upon base types, how to create and manipulate them, and the tibble as a modern alternative.
Causal Reasoning with DAGs	Learn to use Directed Acyclic Graphs (DAGs) to distinguish causal from associational claims, identify confounders, mediators, and colliders, and determine what to control for in a regression model.
Regression Residuals and Model Diagnostics	Understand the distinction between residuals and error terms, learn the key assumptions of linear regression, and apply visual and quantitative diagnostic tests to evaluate model quality.
Setting Up an R Project	How to create a well-structured R project directory, understand working directories and file paths, and use the here package to write portable, reproducible code.
Using AI Tools for R Coding	How to use AI assistants deliberately and critically in R programming — understanding what AI can and cannot do, three modes of effective use, the failure modes to watch for, and the one habit that makes AI assistance durable.
Visualization with ggplot2	How to build publication-quality plots in R using ggplot2: the layer-by-layer workflow, mapping aesthetics, adding geoms, adjusting scales, themes, and saving plots.
Accessing Development Data in R	A practical guide to gathering cross-country development data: querying the World Bank API with the WDI package, caching downloads so they run only once, and obtaining data from the UNDP, WID.world, Our World in Data, and the Atlas of Economic Complexity.
Human Development and Social Indicators	How the Human Development Index and its gender-sensitive relatives are constructed, what the choice of geometric aggregation encodes, and why directly measured education, health, and infrastructure indicators are often more informative than any composite.
Income, Output, and Complexity Indicators	What GDP and GNI per capita actually measure, why the real/nominal and PPP adjustments matter, and how the Economic Complexity Index captures productive capabilities that income measures miss.
Measuring Inequality and Poverty	How the Lorenz curve, the Gini coefficient, top shares, and the Palma ratio describe a distribution, how absolute poverty lines and the Multidimensional Poverty Index identify deprivation, and why these measures disagree.
The Theory-Ladenness of Observation	Why there is no such thing as a neutral measurement: how theoretical commitments enter every indicator through the choices of concept, operationalization, normalization, and aggregation — and what this means for empirical work.
Measuring Institutions with the Worldwide Governance Indicators	How the World Bank’s Worldwide Governance Indicators turn a contested concept into six numbers: what the aggregation procedure assumes, which conception of good governance the dimensions encode, how to get the data into R, and what a correlation between governance scores and development outcomes can and cannot establish.

Citation

BibTeX citation:

@inreference{graebner2026-introduction-to-regression-in-r,
  author = {Gräbner-Radkowitsch, Claudius},
  editor = {Gräbner-Radkowitsch, Claudius},
  title = {Learning {Track:} {Introduction} to {Regression} in {R}},
  booktitle = {Method and Inquiry},
  date = {2026-06-16},
  url = {https://euf-methodology-hub.netlify.app/tracks/introduction-to-regression-in-r},
  langid = {en}
}

For attribution, please cite this work as:

Gräbner-Radkowitsch, Claudius. 2026. “Learning Track: Introduction to Regression in R.” In Method and Inquiry, edited by Claudius Gräbner-Radkowitsch. June 16. https://euf-methodology-hub.netlify.app/tracks/introduction-to-regression-in-r.