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Computational statistics with R / [edited by] Marepalli B. Rao and C.R. Rao.

Contributor(s): Rao, Marepalli, B [editor] | Rao, C.R [editor].
Series: Handbook of statistics ; v 32.Publisher: Amsterdam : Elsevier, ©2014Description: xvii, 394 p. ; illustrations.ISBN: 9780444634313.Subject(s): R (Computer program language) | Mathematical statistics -- Data processingDDC classification: 000SA.055
Contents:
1. Introduction to R -- 2. R graphics -- 3. Graphics miscellanea -- 4. Matrix algebra topics in statistics and economics using R -- 5. Sample size calculations with R: level 1 -- 6. Sample size calculations with R: level 2 -- 7. Bionomial regression in R -- 8. Computing tolerance intervals and regions using R -- 9. Modeling the probability of second cancer in controlled clinical trials -- 10. Bayesian networks.
Summary: 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.
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Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
000SA.055 R215 (Browse shelf) Available 137289
Total holds: 0

Includes bibliographical references and index.

1. Introduction to R --
2. R graphics --
3. Graphics miscellanea --
4. Matrix algebra topics in statistics and economics using R --
5. Sample size calculations with R: level 1 --
6. Sample size calculations with R: level 2 --
7. Bionomial regression in R --
8. Computing tolerance intervals and regions using R --
9. Modeling the probability of second cancer in controlled clinical trials --
10. Bayesian networks.

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.

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