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Data Analysis Using Regression and Multilevel/Hierarchical Models

Data Analysis Using Regression and Multilevel/Hierarchical Models

Data Analysis Using Regression and Multilevel/Hierarchical Models
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Data Analysis Using Regression and Multilevel/Hierarchical Models Paperback - 2006 - 1st Edition

by Andrew Gelman; Jennifer Hill

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Cambridge University Press. 1. Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Details

  • Title Data Analysis Using Regression and Multilevel/Hierarchical Models
  • Author Andrew Gelman; Jennifer Hill
  • Binding Paperback
  • Edition number 1st
  • Edition 1
  • Condition Used - Very good
  • Pages 648
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press, Cambridge
  • Publication date 2006-12-18
  • Features Bibliography, Index, Table of Contents
  • Bookseller's Inventory # 052168689X-8-1
  • ISBN 9780521686891 / 052168689X
  • Weight 2.65 lbs (1.20 kg)
  • Dimensions 9.9 x 6.9 x 1.4 in (25.15 x 17.53 x 3.56 cm)
  • Category Politics / Current Events
  • Library of Congress subjects Regression analysis, Multilevel models (Statistics)
  • Library of Congress Catalogue Number 2006040566
  • Dewey Decimal Code 519.536
  • Quantity available 2

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Reader reviews for Data Analysis Using Regression and Multilevel/Hierarchical Models

From the publisher

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: http: //www.stat.columbia.edu/ gelman/arm/
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