# Computational Statistics with R, Vol 32. Handbook of Statistics

• ID: 2936231
• Book
• 412 Pages
• Elsevier Science and Technology
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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.

• No other source is needed to gain practical experience
• Focus on the essentials in graphical outlays

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Ch. 1: Introduction to R, Chaitra Nagaraja Ch. 2: R Graphics, Deepayan Sarkar Ch. 3: Graphics Miscellanea, Palash Mallick and M.B. Rao Ch. 4: Matrix Algebra Topics in Statistics and Economics Using R, Hrishikesh Vinod Ch. 5: Sample Size Calculations with R: Level 1, M.B. Rao and Subramanyam Kasala Ch. 6: Sample Size Calculations with R: Level 2, M.B. Rao and Hansen Bannerman-Thompson Ch. 7: Binomial Regression, Ravi Varadhan Ch. 8: Tolerance Limits with R, Derek Young Ch. 9: Modeling the Probability of Second Cancer in Controlled Clinical Trials, Kao-Tai Tsai and Karl E Peace Ch. 10: Bayesian Networks and R, M.B. Rao

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