Skip to main content

Save up to 30% on Elsevier print and eBooks with free shipping. No promo code needed.

Save up to 30% on print and eBooks.

Computational Statistics with R

  • 1st Edition, Volume 32 - November 25, 2014
  • Editors: Marepalli B. Rao, C.R. Rao
  • Language: English
  • Hardback ISBN:
    9 7 8 - 0 - 4 4 4 - 6 3 4 3 1 - 3
  • eBook ISBN:
    9 7 8 - 0 - 4 4 4 - 6 3 4 4 1 - 2

R is open source statistical computing software. Since the R core group was formed in 1997, R has been extended by a very large number of packages with extensive documentation al… Read more

Computational Statistics with R

Purchase options

LIMITED OFFER

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

Institutional subscription on ScienceDirect

Request a sales quote

R is open source statistical computing software. Since the R core group was formed in 1997, R has been extended by a very large number of packages with extensive documentation along with examples freely available on the internet. It offers a large number of statistical and numerical methods and graphical tools and visualization of extraordinarily high quality. R was recently ranked in 14th place by the Transparent Language Popularity Index and 6th as a scripting language, after PHP, Python, and Perl.

The book is designed so that it can be used right away by novices while appealing to experienced users as well. Each article begins with a data example that can be downloaded directly from the R website. Data analysis questions are articulated following the presentation of the data. The necessary R commands are spelled out and executed and the output is presented and discussed. Other examples of data sets with a different flavor and different set of commands but following the theme of the article are presented as well. Each chapter predents a hands-on-experience. R has superb graphical outlays and the book brings out the essentials in this arena. The end user can benefit immensely by applying the graphics to enhance research findings. The core statistical methodologies such as regression, survival analysis, and discrete data are all covered.