Statistics: An Introduction using R | 
 enlarge | Author: Michael J. Crawley
Publisher: Wiley
Category: Book
List Price: $50.00
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Rating:  7 reviews
Sales Rank: 96151
Media: Paperback
Edition: 1
Pages: 342
Number Of Items: 1
Shipping Weight (lbs): 1.4
Dimensions (in): 9.6 x 6.4 x 0.8
ISBN: 0470022981
Dewey Decimal Number: 519.5
EAN: 9780470022986
Publication Date: May 6, 2005
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Product Description
Computer software is an essential tool for many statistical modelling and data analysis techniques, aiding in the implementation of large data sets in order to obtain useful results. R is one of the most powerful and flexible statistical software packages available, and enables the user to apply a wide variety of statistical methods ranging from simple regression to generalized linear modelling. Statistics: An Introduction using R is a clear and concise introductory textbook to statistical analysis using this powerful and free software, and follows on from the success of the author's previous best-selling title Statistical Computing.
* Features step-by-step instructions that assume no mathematics, statistics or programming background, helping the non-statistician to fully understand the methodology.
* Uses a series of realistic examples, developing step-wise from the simplest cases, with the emphasis on checking the assumptions (e.g. constancy of variance and normality of errors) and the adequacy of the model chosen to fit the data.
* The emphasis throughout is on estimation of effect sizes and confidence intervals, rather than on hypothesis testing.
* Covers the full range of statistical techniques likely to be need to analyse the data from research projects, including elementary material like t-tests and chi-squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling.
* Includes numerous worked examples and exercises within each chapter.
* Accompanied by a website featuring worked examples, data sets, exercises and solutions:
http://www.imperial.ac.uk/bio/research/crawley/statistics
Statistics: An Introduction using R is the first text to offer such a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a broad range of disciplines. It is primarily aimed at undergraduate students in medicine, engineering, economics and biology - but will also appeal to postgraduates who have not previously covered this area, or wish to switch to using R.
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| Customer Reviews: Read 2 more reviews...
 More about R than about Statistics January 10, 2007
Alethephant (Virginia Beach, VA USA)
19 out of 20 found this review helpful
The title of this book is a misnomer. It is not an introduction to statistics at all, although it does do a very clear review of courses in descriptive statistics, regression, ANOVA, ANCOVA and GLM. If you don't know statistics, and want to learn, this is not the book for you.
This is, however, a truly excellent book that gets you up to speed very quickly on a wide variety of statistical applications using R as the tool for solution. If you have a reasonable background in statistics and want to use R as a substitute for SAS, SPSS, BMD or other package, this book will teach you how within a week. (Make sure you download the examples from the referenced website.
Excellent introduction to R June 13, 2006
L. W. Degroote
17 out of 18 found this review helpful
Well written and easy to understand. Written by an ecologist for anyone with some statistical background with anova, regression, and more. Great review of basic statistics with code in R and exercises/ data available online at M. Crawley's website. In my opinion, better than Data Analysis and Graphics Using R.
Excellent introduction January 19, 2007
Harold Baize (Chico CA USA)
3 out of 6 found this review helpful
This book is the best I have found for an introduction to the R statistical programming environment. It is also a very good textbook for introductory statistics. The supplemental material at his web site is excellent as well.
 Great review of important statistical concepts and intro to R December 14, 2007
Steven R. Mocarski (New York, NY)
If you have already had some experience with statistical methods and are looking for a refresher or a way to quickly pickup the basics of R, this is the place to go. It has a wonderfully conversational tone that is missing from far too many scientifically oriented books, and he brings quite a few insights into the practice of statistics that are more difficult to pickup from the heavily theoretical books.
I would agree with a previous reviewer that there is a bit more space than necessary dedicated to relatively simple concepts, but such minor transgressions are easily overlooked given the overall effectiveness of the book.
I would recommend this book as a refresher/introduction to R, or as a companion book to a college course on statistical methods. The author doesn't cover theory at all (on purpose), so keep in mind this is purely a practical book.
I would have given the book 5 stars if it weren't for a few typos that might confuse beginners or people who have a tendency to read when you're a bit to tired to do so (for example, on the bottom of p59 he says lower bound when he meant upper, nothing you wouldn't catch with a careful re-reading).
 decent book but uneven May 1, 2007
kleytos (United States)
18 out of 19 found this review helpful
This book purports to be an introduction to statistics using R. R has exploded in popularity and today is probably the most powerful system available for doing statistics, having surpassed the older Splus and SAS. Thus you do well to learn R early on as you begin statistics; it well suits the novice and the expert. To make things even better, R is both open source and free with an excellent, supportive online community of many people. The online mailing lists are a treasure trove of valuable resources. There are now several introductory books to R, including one by Verzani, one by Dalgaard, and one by Crawley.
Crawley's book is a _very_ rapid tour through a lot of statistics. There is no real way that a beginner could properly digest the material. Moreover, he often assumes far too much and then assumes far too little. For example in one early chapter he covers the basics of General Linear Models (GLMs), an intermediate to advanced concept. At the beginning of the next chapter, he is explaining basics about the slope of a line! There are a lot of similar examples that left me scratching my head.
There are good pearls in the book that are quite nice, however this book should really be for those with some exposure to statistics.
A better introductory book is "Using R for Introductory Statistics" by John Verzani. That book was more clear and better organized.
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