- Paperback: 304 pages
- Publisher: Chapman and Hall/CRC; 1 edition (February 17, 2006)
- Language: English
- ISBN-10: 1584885394
- ISBN-13: 978-1584885399
- Product Dimensions: 9.2 x 5.9 x 0.6 inches
- Shipping Weight: 8.8 ounces
- Average Customer Review: 7 customer reviews
- Amazon Best Sellers Rank: #2,716,456 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
A Handbook of Statistical Analyses Using R 1st Edition
Use the Amazon App to scan ISBNs and compare prices.
There is a newer edition of this item:
The Amazon Book Review
Author interviews, book reviews, editors picks, and more. Read it now
Customers who viewed this item also viewed
What other items do customers buy after viewing this item?
Useful examples are presented to assist understanding.
Everitt and Hothorn have written an excellent tutorial on using R to analyze data using a wide range of standard statistical methods. They use numerous examples throughout the text, present 100 figures, and show 54 tables to augment discussion. All this is done in a book of only 275 pages in length. I highly recommend the text for anyone learning R, and who want to use it for the sophisticated analysis of data.
-Joseph M. Hilbe, Emeritus Professor, University of Hawaii and Adjunct Professor, Sociology and Statistics, Arizona State University, Journal of Statistical Software, Vol. 16, August 2006
The book is clearly meant to help a true beginner get started with the R package. It begins appropriately with a chapter presenting a description of R and installation instructions, the help (simple help) and vignette (detailed help) commands, and other available documentation. This chapter also discusses basic data handling techniques and methods for summarizing data. The remainder of the book consists of 14 chapters, each of which describes a different type of analysis. The chapters are generally well laid out and easy to understand. The book covers ANOVA/MANOVA, several forms of regression, an assortment of multivariate analyses, and various other forms of statistical analysis. For the experienced analyst wanting to learn R, this book is a useful, compact introduction.
This book, using analyses of real sets of data, takes the reader through many of the standard forms of statistical methodology using R. a very valuable reference. The book is particularly good at highlighting the graphical capabilities of the language.
-P. Marriott (University of Waterloo, Canada), Short Book Reviews
Browse award-winning titles. See more
Top customer reviews
There was a problem filtering reviews right now. Please try again later.
_A Handbook of Statistical Analyses Using R_ sits nicely between the traditional introductory tomes for R (Introductory Statistics with R by Peter Dalgaard, or Statistics: An Introduction using R by Michael J. Crawley being two of the best) and the more advanced single topic texts which have a tendency to focus on one particular modeling technique.
As a workbook, the examples are short enough to be worked through in anywhere from 30 minutes to two hours. And while they often assume that the reader is familiar with certain aspects of statistical analysis, a quick refresher is provided for most topics before the exercises.
As a quick reference used to give examples of how to analyze different types of data, the book stands out for having a diverse set of worked examples that give a great jump start into working with R if you need a sample to get going.
If you work with R long enough, you'll find that you need a variety of reference sources to draw upon. _A Handbook of Statistical Analyses Using R_ is a solid addition to that reference library.
This book helps open up sensible techniques thst can be applied to a wide variety of problems that the applied researcher might need. The only major technique that is missing here are the Bayesian hierarchical models that have been used extensively in the medical device arm of the FDA (CDRH) are not covered in this fine text.
There is some requisite for at least a beginners knowledge of R and statistics.