Data Analysis Using Regression and Multilevel/Hierarchical Models
Gelman, Andrew
ISBN 10: 052168689X ISBN 13: 9780521686891
Published by Cambridge University Press, 2006
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This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
About the Authors:
Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia University. He has published over 150 articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy. His other books are Bayesian Data Analysis (1995, second edition 2003) and Teaching Statistics: A Bag of Tricks (2002).
Jennifer Hill is Assistant Professor of Public Affairs in the Department of International and Public Affairs at Columbia University. She has co-authored articles that have appeared in the Journal of the American Statistical Association, American Political Science Review, American Journal of Public Health, Developmental Psychology, the Economic Journal and the Journal of Policy Analysis and Management, among others.
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Bibliographic Details
Title: Data Analysis Using Regression and ...
Publisher: Cambridge University Press
Publication Date: 2006
Binding: Paperback
Condition: New
Edition: 1st Edition.
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Data Analysis Using Regression and Multilevel/Hierarchical Models
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Cambridge University Press, 2009
ISBN 10: 052168689X
ISBN 13: 9780521686891
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Data Analysis Using Regression and Multilevel/Hierarchical Models (Paperback)
Published by
Cambridge University Press, Cambridge, 2006
ISBN 10: 052168689X
ISBN 13: 9780521686891
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Paperback. Condition: new. Paperback. 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. Author resource page: / gelman/arm/ 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. Author resource page: ~gelman/arm/ This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780521686891
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Data Analysis Using Regression and Multilevel/Hierarchical Models
Published by
Cambridge University Press, 2006
ISBN 10: 052168689X
ISBN 13: 9780521686891
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First Edition
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Condition: New. 2006. 1st Edition. paperback. This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. Series Editor(s): Alvarez, R. Michael; Beck, Nathaniel L.; Wu, Lawrence L. Series: Analytical Methods for Social Research. Num Pages: 648 pages, 160 exercises. BIC Classification: JHBC; PBK. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 254 x 179 x 37. Weight in Grams: 1120. Series: Analytical Methods for Social Research. 648 pages, 160 exercises. For the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. Cateogry: (P) Professional & Vocational; (U) Tertiary Education (US: College). BIC Classification: JHBC; PBK. Dimension: 254 x 179 x 37. Weight: 1132. Series Editor(s) :Alvarez, R. Michael; Beck, Nathaniel L.; Wu, Lawrence L. . . . . . Seller Inventory # V9780521686891
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Data Analysis Using Regression and Multilevel/Hierarchical Models (Paperback)
Published by
Cambridge University Press, Cambridge, 2006
ISBN 10: 052168689X
ISBN 13: 9780521686891
New
Paperback
First Edition
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. 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. Author resource page: / gelman/arm/ 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. Author resource page: ~gelman/arm/ This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521686891
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Data Analysis Using Regression and Multilevel/Hierarchical Models (Paperback)
Published by
Cambridge University Press, Cambridge, 2006
ISBN 10: 052168689X
ISBN 13: 9780521686891
New
Paperback
First Edition
Seller: AussieBookSeller, Truganina, VIC, Australia
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. 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. Author resource page: / gelman/arm/ 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. Author resource page: ~gelman/arm/ This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9780521686891