# Statistics: An Introduction using R

ISBN: 978-0-470-02298-6
342 pages
April 2005
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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Preface.

Chapter 1. Fundamentals.

Chapter 2. Dataframes.

Chapter 3. Central Tendency.

Chapter 4. Variance.

Chapter 5. Single Samples.

Chapter 6. Two Samples.

Chapter 7. Statistical Modelling.

Chapter 8. Regression.

Chapter 9. Analysis of Variance.

Chapter 10. Analysis of Covariance.

Chapter 11. Multiple Regression.

Chapter 12. Contrasts.

Chapter 13. Count Data.

Chapter 14. Proportion Data.

Chapter 15. Death and Failure Data.

Chapter 16. Binary Response Variable.

Appendix 1: Fundamentals of the R Language.

References and Further Reading.

Index.

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• 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:
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"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)

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