Proposes a consistent workflow that can be applied to (almost) any statistical or machine learning model. Readers will learn how to transform complex parameter estimates into quantities that are readily interpretable, intuitive, and understandable.
Proposes a consistent workflow that can be applied to (almost) any statistical or machine learning model. Readers will learn how to transform complex parameter estimates into quantities that are readily interpretable, intuitive, and understandable.
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.
Inhaltsangabe
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
