* 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:
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.
Keywords: Computational & Graphical Statistics