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A01=Ken Kleinman
A01=Nicholas J. Horton
advanced statistical programming
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Author_Ken Kleinman
Author_Nicholas J. Horton
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Category1=Non-Fiction
Category=UFM
COP=United States
data
Data Ds
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data wrangling
database integration
dataset
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finite mixture modeling
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Generalized Linear Mixed Model
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inferential procedures
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Markov chain Monte Carlo
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multivariate methods
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R
reproducible research
SAS
Sas 7bdat
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Product details

  • ISBN 9781466584495
  • Weight: 1037g
  • Dimensions: 178 x 254mm
  • Publication Date: 17 Jul 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent TasksThe first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications.

New to the Second EditionThis edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples.

Enables Easy Mobility between the Two SystemsThrough the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website.

Ken Kleinman, Nicholas J. Horton

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