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.

Product Details

• Amazon Sales Rank: #18571 in Books
• Published on: 2005-03-11
• Original language: English
• Number of items: 1
• Binding: Paperback
• 342 pages

Review
"I would recommend this book to those who need to teach statistics via the medium of R and those self learners who want to acquire the basic techniques of statistics together with powerful statistical software." (Technometrics, May 2006)

"…will provide you with enhanced statistical insights…and access to a free and powerful computing language." (Clinical Chemistry, May 2006)

"...I know of no better book of its kind..." (Journal of the Royal Statistical Society, Vol 169 (1), January 2006)

"…offers a demanding, non–calculus–based coverage of such standard topics as hypothesis testing, modeling, regression, ANOVA, and count data." (CHOICE, November 2005)

Review
"I would recommend this book to those who need to teach statistics via the medium of R and those self learners who want to acquire the basic techniques of statistics together with powerful statistical software." (Technometrics, May 2006)

"…will provide you with enhanced statistical insights…and access to a free and powerful computing language." (Clinical Chemistry, May 2006)

"...I know of no better book of its kind..." (Journal of the Royal Statistical Society, Vol 169 (1), January 2006)

"…offers a demanding, non–calculus–based coverage of such standard topics as hypothesis testing, modeling, regression, ANOVA, and count data." (CHOICE, November 2005)

From the Back Cover
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.

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.

Customer Reviews

great for R beginner!
This is a book that you learn R as well as statistics, with all examples available very applicable to real life scenarios. I am a first year PhD student in biology who needs to start picking up Maths as well as a program to analysis. This book so far enables me to learn R as well as recap some of the fundamentals in statistics.

Patchy presentation, but good overall
It's a long time since I did any statistics, and it wasn't in much depth. My workplace has a lot of time series data stored in a database; I plan on using R to provide automatic anomaly detection, to aid in capacity planning, and business development -- classic data warehouse tasks, in other words.

This book is very helpful in some respects. The pragmatic approach described (in particular the sections on model fitting) seems very good. I enjoyed the inclusion of some historical background on the derivation of the statistical methods, and the chatty style didn't grate. Some of the practical hints and tips, eg how not to build a data frame, seemed as if they would be good for complete newcomers to R. The book essentially fulfils its title as an introduction to statistics using R.

My particular interest in time series isn't addressed in this book -- I also bought "The Analysis of Time Series: An Introduction".

Now the negatives. I generally like "Statistics: An Introduction Using R", so don't take this as advice not to buy it -- I just hope these things are fixed in a later edition. My quibbles are largely stylistic or production related.

* Despite the author's assertion that the book assumes no mathematical knowledge from time to time it dives into notation that isn't adequately explained for the complete novice. It seems as if the depth of prior knowledge varies from chapter to chapter; at some points in the book I felt that it was pitched at a much less experienced audience, and at others that it was right over my head. It seems to fall between the two stools of being an introduction to stats in general, and an introduction to stats with R on the other. Assuming no /a priori/ knowledge seems to me to be the safer course, and was certainly what I was looking for.
* The Helvetica font for presenting the source code is irritating; in several cases the characters are ambiguous, and it's generally hard to read. Transcripts of output from the software are presented in a fixed-width font; surely it would be natural to do the same for the input? In fact, the typography generally is poor, and is put to shame by the beautiful Dalgaard book "Introductory Statistics with R".
* Although examples are apparently available online, some extra information in the book on graphical techniques (eg the code to generate some of the figures) would have been appreciated.
* A nitpick: the assumption is made that R is running on the Windows platform (presumably the author's university labs run on this OS). It would be nice to see a short appendix of platform-specfic information, eg about running R on Mac OS X, Solaris, or Linux. The information on data entry, for example, makes the assuption that Microsoft Excel will be the tool of choice -- I plan on pullig information directly out of a database, and would rather see a section on interfacing R to a proper data source, rather than a glorified grid control.
* A final nitpick -- in one place "lose" is misspelt as "loose". I physically wince on reading this page. I haven't spotted any other typos though.

The best Stats Book I've ever read
This book achieves what I considered impossible - it genuinely explains how to use the magnificent and FREE statistics package R AND teach you statistics at the same time from scratch. Michael Crawley is a born teacher and writer. As I was working through the book, I kept thinking 'How is the man managing it? This is astonishing' Look at most Stats books and try and find a few with ANY reviewers.. This is the best Stats book I've ever read. BUY IT