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Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman, Jennifer Hill

Published by Cambridge University Press
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Title: Data Analysis Using Regression and ...
Publisher: Cambridge University Press
Binding: paperback
Condition: VeryGood

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Gelman, Andrew & Jennifer Hill
Published by Cambridge University Press, 2006
ISBN 10: 052168689X ISBN 13: 9780521686891
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Andrew Gelman; Jennifer Hill
Published by Cambridge University Press, 2006
ISBN 10: 052168689X ISBN 13: 9780521686891
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Gelman, Andrew; Hill, Jennifer
Published by Cambridge University Press, 2006
ISBN 10: 052168689X ISBN 13: 9780521686891
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Gelman, Andrew; Hill, Jennifer
Published by Cambridge University Press, 2006
ISBN 10: 052168689X ISBN 13: 9780521686891
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Andrew Gelman; Jennifer Hill
Published by Cambridge University Press, 2006
ISBN 10: 052168689X ISBN 13: 9780521686891
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Hill, Jennifer, Gelman, Andrew
Published by Cambridge University Press, 2006
ISBN 10: 052168689X ISBN 13: 9780521686891
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Gelman, Andrew; Hill, Jennifer
Published by Cambridge University Press, 2006
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Gelman, Andrew; Hill, Jennifer
Published by Cambridge University Press, 2006
ISBN 10: 052168689X ISBN 13: 9780521686891
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Condition: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers. Seller Inventory # 4403277-5

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Andrew Gelman; Jennifer Hill
Published by Cambridge University Press, 2006
ISBN 10: 0521867061 ISBN 13: 9780521867061
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Hardcover. Condition: Fine. 1st Edition. Hardcover. Condition: Fine (Like New). Minor dusting; no damage to the cover or pages. Data Analysis Using Regression and Multilevel/Hierarchical Models (First published in 2007) is a comprehensive guide for applied researchers looking to perform data analysis using linear and nonlinear regression, as well as multilevel models. The book covers a broad range of statistical methods, including causal inference (regression, poststratification, matching, regression discontinuity, and instrumental variables), multilevel logistic regression, and missing-data imputation. It features numerous real-world data examples drawn from the authors own research, each accompanied by programming code to aid implementation. With clear instruction and practical tips on model building, fitting, and interpretation, this volume is ideal for readers using freely available software packages to conduct advanced data analysis. Box #39. Seller Inventory # 505

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Andrew Gelman; Jennifer Hill
Published by Cambridge University Press, 2006
ISBN 10: 052168689X ISBN 13: 9780521686891
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