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Full Description
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.
Contents
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