Regression and other stories - Grand Format

Edition en anglais

Andrew Gelman

,

Jennifer Hill

,

Aki Vehtari

Note moyenne 
Many textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book... Lire la suite
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Résumé

Many textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is a book about how to use regression to solve real problems of comparison, estimation, prediction, and causal inference. It focuses on practical issues such as sample size and missing data and a wide range of goals and techniques.
It jumps right in to methods and computer code you can use fresh out of the box. Key features : - Real examples, real stories from the authors' real-world experience demonstrate what can be achieved by regression and what the limitations are ; - Computation with the popular open-source programs R and Stan instead of deriving formulas, with all code available online ; - Emphasis on using graphics and presentation to understand and check models that have been fit to data ; - Practical advice for understanding assumptions and implementing methods for experiments and observational studies ; - Smooth transition to logistic regression and generalized linear model ; - Clear presentation of key ideas in data collection, sampling, generalization, and causal inference.

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À propos des auteurs

The authors are experienced researchers who have published articles in hundreds of different scientific journals in fields including statistics, computer science, policy, public health, political science, economics, sociology, and engineering. They have also published articles in the Washington Post, New York Times, Siate, and other public venues. Their previous books include Bayesian Data Analysis, Teaching Statistics : A Bag of Tricks, and Data Analysis and Regression Using Multilevel / Hierarchical Models.
Andrew Gelman is Higgins Professor of Statistics and Professor of Political Science at Columbia University. Jennifer Hill is Professor of Applied Statistics at New York University. Aki Vehtari is Associate Professor in Computational Probabilistic Modeling at Aalto University.

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