logo
Product categories

EbookNice.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link.  https://ebooknice.com/page/post?id=faq


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookNice Team

(Ebook) Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman, Jennifer Hill ISBN 9780521867061, 0521867061

  • SKU: EBN-1462230
Zoomable Image
$ 32 $ 40 (-20%)

Status:

Available

0.0

0 reviews
Instant download (eBook) Data Analysis Using Regression and Multilevel/Hierarchical Models after payment.
Authors:Andrew Gelman, Jennifer Hill
Pages:651 pages.
Year:2007
Editon:1
Publisher:Cambridge University Press
Language:english
File Size:8.78 MB
Format:pdf
ISBNS:9780521867061, 0521867061
Categories: Ebooks

Product desciption

(Ebook) Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman, Jennifer Hill ISBN 9780521867061, 0521867061

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/

*Free conversion of into popular formats such as PDF, DOCX, DOC, AZW, EPUB, and MOBI after payment.

Related Products