Statistics

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

A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R

This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology.  The book covers the full range of statistical techniques likely to be needed 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.

Michael J. Crawley, FRS, Department of Biological Sciences, Imperial College of Science, Technology and Medicine. Author of three bestselling Wiley statistics titles and five life science books.

A revised and updated edition of this bestselling introduction to statistical analysis using the leading free software package R In recent years R has become one of the most popular, powerful and flexible statistical software packages available. It enables users to apply a wide variety of statistical methods, ranging from simple regression to generalized linear modelling, and has been widely adopted by life scientists and social scientists. This new edition offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material such as t tests and chi-squared tests, intermediate methods such as regression and analysis of variance, and more advanced techniques such as generalized linear modelling. Numerous worked examples and exercises are included within each chapter. Comprehensively revised to include more detailed introductory material on working with R Updated to be compatible with the current R Version 3 Complete coverage of all the essential statistical methods Focus on linear models (regression, analysis of variance and analysis of covariance) and generalized linear models (for count data, proportion data and age-at-death data) Now includes more detail on experimental design Accompanied by a website featuring worked examples, data sets, exercises and solutions Statistics: An introduction using R is primarily aimed at undergraduate students in medicine, engineering, economics and biology - but will also appeal to postgraduates in these areas who wish to switch to using R.

A revised and updated edition of this bestselling introduction to statistical analysis using the leading free software package R In recent years R has become one of the most popular, powerful and flexible statistical software packages available. It enables users to apply a wide variety of statistical methods, ranging from simple regression to generalized linear modelling, and has been widely adopted by life scientists and social scientists. This new edition offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material such as t tests and chi-squared tests, intermediate methods such as regression and analysis of variance, and more advanced techniques such as generalized linear modelling. Numerous worked examples and exercises are included within each chapter. Comprehensively revised to include more detailed introductory material on working with R Updated to be compatible with the current R Version 3 Complete coverage of all the essential statistical methods Focus on linear models (regression, analysis of variance and analysis of covariance) and generalized linear models (for count data, proportion data and age-at-death data) Now includes more detail on experimental design Accompanied by a website featuring worked examples, data sets, exercises and solutions Statistics: An introduction using R is primarily aimed at undergraduate students in medicine, engineering, economics and biology but will also appeal to postgraduates in these areas who wish to switch to using R.

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