Data analysis using regression and multilevel/hierarchical models

Data analysis using regression and multilevel/hierarchical models

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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Main Authors: Gelman, A. 182422, Hill, J. 182423
Format: Texto biblioteca
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Published: New York (USA) Cambridge University Press 2007
Subjects:Statistical methods, Regression analysis, Linear models, Data analysis,
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spelling unfao:6778902021-05-05T06:52:06ZData analysis using regression and multilevel/hierarchical models Gelman, A. 182422 Hill, J. 182423 textNew York (USA) Cambridge University Press2007 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.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.Statistical methodsRegression analysisLinear modelsData analysisURN:ISBN:978-0-521-68689-1
institution FAO IT
collection Koha
country Italia
countrycode IT
component Bibliográfico
access En linea
Fisico
databasecode cat-fao-it
tag biblioteca
region Europa del Sur
libraryname David Lubin Memorial Library of FAO
language
topic Statistical methods
Regression analysis
Linear models
Data analysis
Statistical methods
Regression analysis
Linear models
Data analysis
spellingShingle Statistical methods
Regression analysis
Linear models
Data analysis
Statistical methods
Regression analysis
Linear models
Data analysis
Gelman, A. 182422
Hill, J. 182423
Data analysis using regression and multilevel/hierarchical models
description 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.
format Texto
topic_facet Statistical methods
Regression analysis
Linear models
Data analysis
author Gelman, A. 182422
Hill, J. 182423
author_facet Gelman, A. 182422
Hill, J. 182423
author_sort Gelman, A. 182422
title Data analysis using regression and multilevel/hierarchical models
title_short Data analysis using regression and multilevel/hierarchical models
title_full Data analysis using regression and multilevel/hierarchical models
title_fullStr Data analysis using regression and multilevel/hierarchical models
title_full_unstemmed Data analysis using regression and multilevel/hierarchical models
title_sort data analysis using regression and multilevel/hierarchical models
publisher New York (USA) Cambridge University Press
publishDate 2007
work_keys_str_mv AT gelmana182422 dataanalysisusingregressionandmultilevelhierarchicalmodels
AT hillj182423 dataanalysisusingregressionandmultilevelhierarchicalmodels
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