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arm: Data Analysis Using Regression and Multilevel/Hierarchical Models

Open access
Gelman, Andrew, Su, Yu-Sung·2007·CRAN: Contributed Packages

Abstract

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

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Metadata

Authors
Gelman, A., & Su, Y.-S.
Venue
CRAN: Contributed Packages
Published
2007-01-01
OpenAlex
W4399608067
Cited by
955
References
0
Type
dataset
Language
en
Open access
gold

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CoChat. (2026). Citation check: arm: Data Analysis Using Regression and Multilevel/Hierarchical Models. https://app.cochat.ai/papers/10.32614/cran.package.arm
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Gelman, A., & Su, Y.-S. (2007). arm: Data Analysis Using Regression and Multilevel/Hierarchical Models. CRAN: Contributed Packages. https://doi.org/10.32614/cran.package.arm

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.