Statistics: An Introduction using RComputer 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 bestselling title Statistical Computing. * Features stepbystep instructions that assume no mathematics, statistics or programming background, helping the nonstatistician to fully understand the methodology. * Uses a series of realistic examples, developing stepwise 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 ttests and chisquared 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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LibraryThing Review
User Review  all4metals  LibraryThingThis book provides an excellent introduction to statistics. I found it easy to read and the examples were well thought out. The author also provides a lot of examples of how to use R for statistics. Read full review
This book is a decent introduction to statistical modelling with R. It contains clear and useful information about how to perform analysis, with guidance in both statistical interpretation and R programming. It outlines pitfalls such as overdispersion, and clearly explains the meaning of the model results (coefficients, depending on the analysis).
However, it suffers from several issues:
 The quality of the R code presented is low. The Arial font is probably not the best font to display code, but more importantly the coding style is bad:
* there is no spacing and indentation which make it hard to read
* it promotes bad practices such as:
** using T instead of TRUE  indicating that the author was a former Splus user where this was not a bad practice 
** naming the data "data" or "table" instead of a descriptive name
** attaching the data sets but never detaching them
For an introduction to R, you could expect a faultless code.
 It uses a lot of data sets without much explanations. Most of the data seem artificial rather than real. Additionally you have to download the data from somewhere, and it is directly read with read.table. The author stores it in the "C:/temp/" folder which is definitely weird. Most equivalent books have a companion module so you can load it more comfortably. It could be useful that way of loading the data was clearly presented in the introduction, or if there were variations depending on the datasets, but it isn't.
 It is a little bit messy. There is only two title levels, and sections are not numbered. It is often difficult to know how a new section relates to the previous one: it is a subsection or a new one?
 Plots are not labelled or numbered. Most often they are directly in the correct place of the text, but at times it can be difficult to identify which image the author is referring to.
 Sometimes there are too much statistical formulas with too little information to be useful. Fortunately this happens only a few times through the book.
 References aren't cited in the text. This makes it difficult to know where to find additional information.
 It keeps repeating some unhelpful "you need a lot of practice" without more explanation or reference.
 The bookbinding is of poor quality and pages quickly detaches.
Overall this is a useful introduction to statistical modelling using R, but with too many imperfection to get a good mark.