Data Analysis Using Regression and Multilevel/Hierarchical Models - Info and Reading Options
By Andrew Gelman
"Data Analysis Using Regression and Multilevel/Hierarchical Models" was published by Cambridge University Press in 2012 - Cambridge, it has 1 pages and the language of the book is English.
“Data Analysis Using Regression and Multilevel/Hierarchical Models” Metadata:
- Title: ➤ Data Analysis Using Regression and Multilevel/Hierarchical Models
- Author: Andrew Gelman
- Language: English
- Number of Pages: 1
- Publisher: Cambridge University Press
- Publish Date: 2012
- Publish Location: Cambridge
“Data Analysis Using Regression and Multilevel/Hierarchical Models” Subjects and Themes:
- Subjects: ➤ Multilevel models (Statistics) - Regression analysis - Analyse de régression - Modèles multiniveaux (Statistique) - Statistical methods - Data analysis - Méthodes statistiques - Analyse statistique - Regressieanalyse - Multiniveau-analyse - Regressionsanalyse - Análise de regressão e de correlação - Multivariat analys - Regressionsanalys - Matematisk statistik - Statistics as Topic - Statistical Data Interpretation - Multilevel analysis - Ha31.3 .g45 2007 - 519.5/36 - 70.03 - Cm 4000 - Mat 628f - Qh 234
Edition Identifiers:
- The Open Library ID: OL34452298M - OL8014111W
- ISBN-13: 9780511790942 - 9780521867061 - 9780521686891
- All ISBNs: 9780511790942 - 9780521867061 - 9780521686891
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"Data Analysis Using Regression and Multilevel/Hierarchical Models" Description:
Open Data:
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
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