» Using R for Introductory Statistics
Using R for Introductory Statistics Details
Binding: HardcoverDewey Decimal Number: 519.5
EAN: 9781584884507
ISBN: 1584884509
Label: Chapman & Hall/CRC
Manufacturer: Chapman & Hall/CRC
Number Of Items: 1
Number Of Pages: 432
Publication Date: 2004-11-29
Publisher: Chapman & Hall/CRC
Studio: Chapman & Hall/CRC
- Introductory Statistics with R (Statistics and Computing)
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- R Graphics (Computer Science and Data Analysis)
- A Handbook of Statistical Analyses Using R
- Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics)
Using R for Introductory Statistics Reviews
Customer Rating:




Summary: Great intro book for R
Comment: This is a great book for learning how to practice intro statistics for R.
If you consider this is a stats book, you will not be satisfied.
You need a stats textbook with this to learn stats.
If you know general things about stats, this book is the best intro for R as a statistical tool.
I also own Introductory Statistics with R by Peter Delgaard.
I recommend to read Using R first then proceed to Delgaard's book.
In this fashion, you will get a brief review of what you learn from Using R and will build up more introductory to intermediate techniques.
If you finish the two books, you are ready to explore any other R and S-Plus books.
Customer Rating:





Summary: Very complete introduction to statistics and GNU R
Comment: This is the perfect book if you are looking for a self-contained, practical introduction to statistics using GNU R.
It contains a lot of examples and exercises for reinforcing the contents. Very clear and organized presentation of topics. It assumes no previous background on statistics at all, and could be used as a complementary text for lab sessions.
It both explains GNU R commands and data types and provides a basic introduction to statistics theory, from a practical point of view. The last one, of course, may also be enhanced by your favorite book for introductory statistics, though it is not absolutely necessary to use this book.
Customer Rating:





Summary: Poorly organized and frustrating
Comment: In an introductory book, it is really important to present concepts in order. This book fails on this count. On more than one occasion, a concept (e.g., "trimmed mean") or a function (e.g., "range(x)") is mentioned without being defined, only to be presented as new later on. This is very frustrating and prevents a new student from working through the book fast. As some other reviewers remarked, the index is a complete disaster, which only makes this worse. It is nice that the book comes with a package of problems. The package lacks answers to most of these problems, though, so one can't check progress easily.
Look elsewhere.
Customer Rating:





Summary: best overall introduction to statistics using R
Comment: This book is an excellent introduction to basic statistics, not assuming a knowledge of calculus, using an intuitive "hands-on approach" using the free computer program R. Statistics should be learned with the fingers on a computer, not merely by memorizing formulas, so you do well to learn statistics with a book in one hand, sitting in front of a computer. John Verzani gives a gentle introduction to statistics using R.
For those unaware, R is a complete, very powerful statistics program that was developed in the 1990s based on an early language called S/Splus, created by John Chambers in the 1970s. S/Splus is an extremely powerful language for doing statistics / numerical research, and was developed explicitly for that purpose. It is far stronger than Matlab for statistical data analysis. R has a vibrant online community with hundreds of free add-on packages (available from the CRAN website). R has grown to be much more powerful than SPSS or SAS in recent years, and is becoming the tool of choice by the experts in the field. It's suitable for beginners too, but doesn't have the point and click style of simpler programs.
There are three main books that are introductions to R. One by Verzani (reviewed here), one by Dalgaard, and one by Crawley. Of the three, I find this one to be the best. It is the most clearly organized and has the best logical presentation of the three. It goes into the right amount of depth without getting bogged down. You can work through all the exercises in the book because the datasets are freely downloadable from the web.
Be sure to do as many of the exercises in the book as you can -- that will really help you to learn statistics well!
Customer Rating:





Summary: Great stater book
Comment: This is a great stater book for basic statistics.And for being used for 28 bucks you can not go wrong.
More Reviews for Using R for Introductory Statistics
Editorial Review for Using R for Introductory Statistics:
The cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability. While it is a valuable tool for students who are first learning statistics, proper introductory materials are needed for its adoption.Using R for Introductory Statistics fills this gap in the literature, making the software accessible to the introductory student. The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The pacing is such that students are able to master data manipulation and exploration before diving into more advanced statistical concepts. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models.
This text lays the foundation for further study and development in statistics using R. Appendices cover installation, graphical user interfaces, and teaching with R, as well as information on writing functions and producing graphics. This is an ideal text for integrating the study of statistics with a powerful computational tool.



