Data analysis using regression and multilevel/hierarchical models
Gelman, Andrew
Hill, Jennifer
Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, 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. Discusses a wide range of linear and non-linear multilevel models Provides R and Winbugs computer codes and contains notes on using SASS and STATA Analyses illustrated with dozens of graphs of data and fitted models Dozens of examples, almost all coming from Gelman/Hill's own applied research
- ISBN: 9780521686891
- Editorial: Cambridge University Press
- Encuadernacion: Rústica
- Páginas: 648
- Fecha Publicación: 01/06/2007
- Nº Volúmenes: 1
- Idioma:
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- INFORMÁTICA /
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