Introductory Statistics with R PDF

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Free download PRETITLE Introductory Statistics with R POSTTITLE from mediafire, rapishare, and mirror link This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.Direct download links available for PRETITLE Introductory Statistics with R (Statistics and Computing) [Kindle Edition] POSTTITLE

• Product Details
• Table of Contents
• Reviews

• File Size: 3373 KB
• Print Length: 364 pages
• Publisher: Springer; 2nd edition (September 1, 2008)
• Sold by: Amazon Digital Services, Inc.
• Language: English
• ASIN: B006ZTRMEW
• Text-to-Speech: Enabled
• X-Ray:
  Not Enabled
• Lending: Not Enabled
• Amazon Best Sellers Rank: #172,594 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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• #15 in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Biological Sciences > Biostatistics
• #33 in Books > Professional & Technical > Professional Science > Biological Sciences > Bioinformatics
• #33 in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Medical eBooks > Research

Introductory Statistics with R PDF

R is a useful freeware that can represent a hurdle to students and/or professionals who do not have formal training in computer programming. This book helps to clear those hurdles, and introduces a solid foundation from which statistics users can build new tools for their specific analyses. The rest of this review is broken up for experienced and new users.

****If you do not have a solid foundation in statistics, this book is not going to help you bridge that gap. Although the title is "Introductory Statistics with R" the author is clear that this is a book to learn how to program intro stats with R, and is not designed to teach any statistics tools. The author assumes you understand statistics and does not clarify statistics terms like p-value, test statistic, degrees of freedom, ANOVA, and the like. ****

New to R:
Although it may sound like a conundrum, the only way to learn a program is to program. Thankfully learning R can be easy, since the program is free, installs well on nearly all machines, and has detailed help files in various languages around the world. This is an excellent book for the R beginner, but I must stress the importance of ACTUALLY PROGRAMMING while you read this book. You CAN NOT read this book cover to cover and expect to learn R, programming doesn't work that way. This book can be a great resource for people who are brand new to R, but it requires hands on utilization of the source codes provided. Thankfully, this step is made that much easier for new users with a detailed explanation of how to obtain the ISWR package used with this text. Like everything in R, packages are free, and contain suites of functions and sometimes data.

I have prepared and delivered introductory courses and workshops on statistics and R for the past 3 years. As part of this work, I have reviewed more than a dozen different introductory R books. This is one of my favourite choices (if not my top one). Pete Dalgaard has been a member of the R Core Team since 1997, being a very active and knowledgeable expert on statistics with R. This quickly becomes apparent in the book, since you will find many tricks and smart procedures to accomplish many R tasks, most notably in the data preparation stage (where you spend 70-80% of all working time).

A previous requirement is to acquire basic knowledge on the statistical tools and techniques presented throughout the book. This volume is focused on performing statistical analyses with R, not offering a complete introductory statistics course. However, each chapter starts with a very useful recap of foundations and theory details for the statistical methods and tools presented in it. You can also find good references for further reading.

Summarizing the main positive points:

* Very clear explanations. The writing style is direct, informative, easy-to-follow.

* Content organization is very clear. Every chapter has been conceived as an independent unit that you can read separately (except for the first introductory chapters to R syntax and routinary operations). Thus, you can either read it cover to cover or just jump directly into the chapter or section of your interest (as a reference).

* There is an accompaning R package 'ISwR', that can be found in CRAN (as usual). It includes all datasets and utility functions presented in the text. This is a must to speed up practical sessions using this text as a reference, as well as for self-study.