1st Edition
Model to Meaning How to Interpret Statistical Models with R and Python
1 Who is this book for?
2 Models and meaning
3 Conceptual frameword
4 Hypothesis and equivalence tests
5 Predictions
6 Counterfactual comparisons
7 Slopes
8 Causal inference with G-computation
9 Experiments
10 Interactions and polynomials
11 Categorical and ordinal outcomes
12 Multilevel regression with poststratification
13 Machine learning
14 Uncertainty
15 Online content
16 Python
Biography
Vincent Arel-Bundock is Professor at the Université de Montréal, where he teaches political economy and research methods. His research focuses on making the interpretation of statistical models more rigorous and accessible. Vincent is the creator of the widely-used marginaleffects software package, available for both R and Python.
"...Model to Meaning is an outstanding contribution to the applied statistics literature. Its emphasis on interpretability, its breadth of models, and its seamless integration with high-quality software make it an ideal reference for graduate courses in statistics, data science, political science, and related fields, as well as a valuable guide for applied researchers working with complex models in practice. The book succeeds not only in explaining how to interpret models, but in reshaping how analysts think about the relationship between models, estimands, and meaning."
-Brenda Betancourt in the Journal of the American Statistical Association, February 2026
