# R by example

Basics Reading files Graphs Probability and statistics Regression Time-series analysis All these examples in one tarfile. Outright non-working code is unlikely, though occasionally my fingers fumble or code-rot occurs. Other useful materials Suggestions for learning R The R project is at : In particular, see the `other docs' there. Over and above the strong set of functions that you get in `off the shelf' R, there is a concept like CPAN (of the perl world) or CTAN (of the tex world), where there is a large, well-organised collection of 3rd party software, written by people all over the world. The dynamism of R and of the surrounding 3rd party packages has thrown up the need for a newsletter, R News. library(help=boot) library(boot) ? But you will learn a lot more by reading the article Resampling Methods in R: The boot package by Angelo J. Ajay Shah, 2005

Software Resources for R Software Resources for R Below is a list of resource pages for using R to do statistics. On each page a set of data are explored with the software. Getting Started Entering data Simple summary statistics Stem and leaf plots Histograms Boxplots Dotplots Logarithmic transformations Saving your data Summarizing Quantitative Data Simple summary statistics Boxplots comparing two groups Transformations Summarizing a Single Categorical Variable Mode Tables Bar charts Pie charts Making and Interpreting Tables for Two Categorical Variables One- and Two-Way Tables Probability and two-way tables Inference for One Proportion Confidence interval Hypothesis test Inference for Two Proportions Chi-Squared Tests One-way (Goodness of Fit) Two-way (Contingency Tables) Inference for a Single Mean Inference for Two Means (Independent Samples) Inference for Paired Differences Scatterplots and Correlation Transformations in R Straightening a curved relationship by transforming a variable Simple Linear Regression

Statistics with R Warning Here are the notes I took while discovering and using the statistical environment R. However, I do not claim any competence in the domains I tackle: I hope you will find those notes useful, but keep you eyes open -- errors and bad advice are still lurking in those pages... Should you want it, I have prepared a quick-and-dirty PDF version of this document. The old, French version is still available, in HTML or as a single file. You may also want all the code in this document. 1. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Multivariate analysis of variance Multivariate analysis of variance or multiple analysis of variance (MANOVA) is a statistical test procedure for comparing multivariate (population) means of several groups. Unlike univariate ANOVA, it uses the variance-covariance between variables in testing the statistical significance of the mean differences. It is a generalized form of univariate analysis of variance (ANOVA). It is used when there are two or more dependent variables. It helps to answer: 1. do changes in the independent variable(s) have significant effects on the dependent variables?; 2. what are the interactions among the dependent variables? Where sums of squares appear in univariate analysis of variance, in multivariate analysis of variance certain positive-definite matrices appear. Analogous to ANOVA, MANOVA is based on the product of model variance matrix, and inverse of the error variance matrix, , or . implies that the product The most common[3][4] statistics are summaries based on the roots (or eigenvalues) of the

Learning R R Programming - Wikibooks, collection of open-content textbooks Welcome to the R programming Wikibook This book is designed to be a practical guide to the R programming language[1]. R is free software designed for statistical computing. How can you share your R experience ? Explain the syntax of a commandCompare the different ways of performing each task using R.Try to make unique examples based on fake data (ie simulated data sets).As with any Wikibook please feel free to make corrections, expand explanations, and make additions where necessary. Some rules : Prerequisites We assume that readers have a background in statistics. We also assume that readers are familiar with computers and that they know how to use software with a command-line interface. See also Larry Wasserman's book All of Statistics[6]The Statistics and the Econometric Theory wikibooks.The Econometrics and Statistics pages on wikipedia. References