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 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. |
Contents
Dataframes | 15 |
Central Tendency | 23 |
2222 | 25 |
Variance | 33 |
Single Samples | 51 |
Calculations using z of the Normal Distribution | 60 |
Bootstrap in Hypothesis Testing with Single Samples | 66 |
Two Samples | 73 |
Analysis of Covariance | 187 |
Multiple Regression | 195 |
Contrasts | 209 |
Count Data | 227 |
Proportion Data | 247 |
Death and Failure Data | 263 |
Fundamentals of the R Language | 281 |
References and Further Reading | 305 |
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Common terms and phrases
analysis of covariance analysis of variance Anova table binomial errors biomass calculate confidence interval continuous explanatory variables Cook's distance correlation count data dataframe degrees of freedom density equation Error t value error variance Estimate Std example F value F-statistic factor levels fitted values freedom Residual deviance garden gender gendermale graph Grazing interaction terms intercept linear model linear predictor link function look M. J. Crawley male Mean Sq mean value measure minimal adequate model model formula model simplification non-linear Normal distribution Null deviance null hypothesis overall mean overdispersion p-value parameter estimates plot population Pr F proportion data pseudoreplication quadratic R-Squared random effects relationship replicates Residual standard error response variable S-PLUS sample saturated model significant significantly different slope smokerTRUE specified SSXY standard error sum of squares Temp tree value Pr(t vector weight obese weightover Wet.days Wind zero