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Sept. 23, 2025, 11:38 a.m.
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| Arel-Bundock V. Model to Meaning...Statistical Models with R and Python 2026.pdf | 8.8 MB |
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SOURCE: Arel-Bundock V. Model to Meaning...Statistical Models with R and Python 2026
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MEDIAINFO
Textbook in PDF format 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. The key idea that underpins this book is that the raw parameter estimates obtained by fitting a model can often be transformed into more interpretable quantities. Presenting results in a way that resonates with the audience enhances clarity, communication, and impact. Unfortunately, computing intuitive statistical quantities, along with standard errors, can be a tedious and error-prone process. Furthermore, whereas many excellent packages exist to fit models, software often behaves in idiosyncratic ways, producing objects with incompatible structures or inconsistent behavior. This makes it difficult for analysts to maintain a consistent workflow across projects. To address this challenge, this book introduces a free and open source software package, marginaleffects, which provides a single point of entry to interpret results from over 100 classes of models in R and Python. This package simplifies the interpretation process by offering a consistent and powerful user interface, reducing the need for customized code, and minimizing the risk of errors. 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. Part I of the book lays the groundwork by encouraging analysts to clearly define their goals, and by introducing a simple conceptual framework to guide model interpretation. The key idea that underpins this framework is that we can often transform the raw parameter estimates obtained by fitting a model into quantities that are much easier to interpret. Converting results to a scale that feels natural to our audience can improve transparency and communication. Part II explains how the conceptual framework can be operationalized through quantities of interest and tests, using concrete examples and real-world datasets. It describes three broad classes of quantities of interest—predictions, Page 2counterfactual comparisons, and slopes—shows how to estimate them, and explains how to design appropriate hypothesis tests to answer our research questions. Part III of the book presents detailed case studies to demonstrate how a consistent workflow can be applied in model-agnostic fashion to various settings: causal inference; experiments; interactions and polynomials; mixed effects models; weighting; categorical outcomes; machine learning; and more. These case studies do not exhaust the range of contexts where the tools and ideas in this book can play an integral role. The website that accompanies this book includes over 30 free chapters with detailed tutorials and notebooks. 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. Contents: Who is this book for? Models and meaning Conceptual frameword Hypothesis and equivalence tests Predictions Counterfactual comparisons Slopes Causal inference with G-computation Experiments Interactions and polynomials Categorical and ordinal outcomes Multilevel regression with poststratification
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