Ebook: Discovering Statistics Using R

"Hot on the heels of the award-winning and best selling Discovering Statistics Using SPSS Third Edition, Andy Field has teamed up with Jeremy Miles (co-author of Discovering Statistics Using SAS) to write Discovering Statistics Using R. Keeping the uniquely humorous and self-depreciating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using the freeware R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioral sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next the importance of exploring and graphing data will be discovered, before moving onto statistical tests that are the foundations of the rest of the book (for e.g. correlation and regression). Readers will then stride confidently into intermediate level analyses such as ANOVA, before ending their journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help the reader gain the necessary conceptual understanding of what they're doing, the emphasis is on applying what's learned to playful and real-world examples that should make the experience more fun than expected."--Publisher's website.;Why is my evil lecturer forcing me to learn statistics? -- Everything you ever wanted to know about statistics (well, sort of) -- The R environment -- Exploring data with graphs -- Exploring assumptions -- Correlation -- Regression -- Logistic regression -- Comparing two means -- Comparing several means: ANOVA (GLM 1) -- Analysis of covariance, ANCOVA (GLM 2) -- Factorial ANOVA (GLM 3) -- Repeated-measures designs (GLM 4) -- Mixed designs (GLM 5) -- Non-parametric tests -- Multivariate analysis of variance (MANOVA) -- Exploratory factor analysis -- Categorical data -- Multilevel linear models.