Vincent Arel-Bundock | Model to Meaning. How to Interpret Statistical Models with R and Python (2025) [PDF]
Автор: Vincent Arel-Bundock
Издательство: Chapman and Hall
ISBN: 978-1032908724, 978-1040434451
Жанр: Statistics, Probability & Statistics
Язык: Английский
Формат: PDF
Качество: Изначально электронное (ebook)
Иллюстрации: Черно-белые
Описание:Our world is complex. To make sense of it, data analysts routinely fit sophisticated statistical or machine learning models. Interpreting the results produced by such models can be challenging, and researchers often struggle to communicate their findings to colleagues and stakeholders. Model to Meaning is a book designed to bridge that gap. It is a practical guide for anyone who needs to translate model outputs into accurate insights that are accessible to a wide audience.
Features:
Presents a simple and powerful conceptual framework to interpret the results from a wide variety of statistical or machine learning models.
Features in-depth case studies covering topics such as causal inference, experiments, interactions, categorical variables, multilevel regression, weighting, and machine learning.
Includes extensive practical examples in both R and Python using the marginal effects software.
Accompanied by comprehensive online documentation, tutorials, and bonus case studies.
Model to Meaning introduces a simple and powerful conceptual framework to help analysts describe the statistical quantities that can shed light on their research questions, estimate those quantities, and communicate the results clearly and rigorously. Based on this framework, the book 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.
Written for data scientists, researchers, and students, the book speaks to newcomers seeking practical skills, and to experienced analysts who are ready to adopt new tools and rethink entrenched habits. It offers useful ideas, concrete workflows, powerful software, and detailed case studies, presented using real-world data and code examples.
Part I: Interpretation
1. Who is this book for?
2. Models and Meaning
3. Conceptual Framework
Part II: Quantities and Tests
4. Hypothesis and Equivalence Tests
5. Predictions
6. Counterfactual Comparisons
7. Slopes
Part III: Case Studies
8. G-Computation
9. Experiments
10. Interactions, Polynomials, and Splines
11. Categorical and Ordinal Outcomes
12. Multilevel Regression and Post-stratification
13. Machine Learning
14. Uncertainty and Conformal Prediction
Part IV: Tutorials & Miscellaneous
Specialized Tutorials
Technical Appendices
Скриншоты:
Время раздачи: с 10 до 20 (минимум до появления первых 3-5 скачавших) 
