Regression and other stories
Most 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 about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, 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 immediately…
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- Full title :Regression and other stories / Andrew Gelman (Columbia University, New York), Jennifer Hill (New York University), Aki Vehtari (Aalto University, Finland).
Summary
Most 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 about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, 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 immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.
Read More ↓Edition Statements
- Publication Date:2021
Publication Statements
- Publisher(s):Cambridge University Press
- Place of Publication:Cambridge
- Language:eng
- Publication Date:2021
Notes
Literaturverzeichnis: Seite 497-515, Register.
Bereits 2020 erschienen.
fundamentals
Overview
Data and measurement
Statistical inference
Simulation
Linear regression
Fitting regression models
Transformations and regression
Generalized linear models
Logistic regression
Causal inference
What comes next?
The Physical Object
- Description:XV, 534 Seiten : Diagramme
Edition Identifiers
- ISBN:9781107676510, 1107676517, 9781107023987, 110702398X
- oclc:on1195968665
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