Python Tutorials for Kids 8+ 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.

CRAN: Manuals edited by the R Development Core Team. The following manuals for R were created on Debian Linux and may differ from the manuals for Mac or Windows on platform-specific pages, but most parts will be identical for all platforms. The correct version of the manuals for each platform are part of the respective R installations. The manuals change with R, hence we provide versions for the most recent released R version (R-release), a very current version for the patched release version (R-patched) and finally a version for the forthcoming R version that is still in development (R-devel). Here they can be downloaded as PDF files, EPUB files, or directly browsed as HTML: Translations of manuals into other languages than English are available from the contributed documentation section (only a few translations are available). The LaTeX or Texinfo sources of the latest version of these documents are contained in every R source distribution (in the subdirectory doc/manual of the extracted archive).

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

How to Think Like a Computer Scientist Learning with Python by Allen Downey, Jeff Elkner and Chris Meyers. This book is now available for sale at Lulu.com. Hardcopies are no longer available from Green Tea Press. How to Think... is an introduction to programming using Python, one of the best languages for beginners. How to Think... is a Free Book available under the GNU Free Documentation License. Please send suggestions, corrections and comments about the book to feedback{at}thinkpython{dot}com. Download The book is available in a variety of electronic formats: Precompiled copies of the book are available in PDF and Postscript . Translations Here are some translations of the book into other (natural) languages: Spanish translation by Gregorio Inda. Other Free Books by Allen Downey are available from Green Tea Press. If you are using this book and would like to make a contribution to support my work, please consider making a donation toward my web hosting bill by clicking on the icon below.

Statistics, R, Graphics and Fun | Yihui Xie Starting data analysis/wrangling with R: Things I wish I'd been told October 14, 2014, [MD] R is a very powerful open source environment for data analysis, statistics and graphing, with thousands of packages available. After my previous blog post about likert-scales and metadata in R, a few of my colleagues mentioned that they were learning R through a Coursera course on data analysis. I have been working quite intensively with R for the last half year, and thought I'd try to document and share a few tricks, and things I wish I'd have known when I started out. I don't pretend to be a statistics whiz – I don't have a strong background in math, and much of my training in statistics was of the social science "click here, then here in SPSS" kind, using flowcharts to divine which tests to run, given the kinds of variables you wanted to compare. So here are some of my suggestions and "lessons learnt", in no particular order. RStudio is an great open source integrated development environment for R. There are lot's of R textbooks and documentation out there.

StatNotes: Topics in Multivariate Analysis, from North Carolina State University Looking for Statnotes? StatNotes, viewed by millions of visitors for the last decade, has now been converted to e-books in Adobe Reader and Kindle Reader format, under the auspices of Statistical Associates Publishers. The e-book format serves many purposes: readers may cite sources by title, publisher, year, and (in Adobe Reader format) page number; e-books may be downloaded to PCs, Ipads, smartphones, and other devices for reference convenience; and intellectual property is protected against piracy, which had become epidemic. Click here to go to the new Statnotes website at . Or you may use the Google search box below to search the website, which contains free e-books and web pages with overview summaries and tables of contents. Or you may click on a specific topic below to view the specific overview/table of contents page.

Bruce Eckel's MindView, Inc: Thinking in Python You can download the current version of Thinking in Python here. This includes the BackTalk comment collection system that I built in Zope. The page describing this project is here. The current version of the book is 0.1. The source code is in the download package. This is not an introductory Python book. However, Learning Python is not exactly a beginning programmer's book, either (although it's possible if you're dedicated). Revision History Revision 0.1.2, December 31 2001.

Quick-R: Home Page Cookbook for R The R Project for Statistical Computing Python for Fun This collection is a presentation of several small Python programs. They are aimed at intermediate programmers; people who have studied Python and are fairly comfortable with basic recursion and object oriented techniques. Each program is very short, never more than a couple of pages and accompanied with a write-up. I have found Python to be an excellent language to express algorithms clearly. From many years of programming these are some of my favorite programs. Many thanks to Paul Carduner and Jeff Elkner for their work on this page, especially for Paul's graphic of Psyltherin (apologies to Harry Potter) and to the teams behind reStructured text and Sphinx to which the web pages in this collection have been adapted. Chris Meyers

R Tutorial — R Tutorial 321a Boyd Graduate Studies University of Georgia Athens, Georgia 30602 Introductory Materials¶ These materials are designed to offer an introduction to the use of R. It is not exhaustive, but is designed to just provide the basics. Thank You! I have received a great deal of feedback from a number of people for various errors, typos, and dumb things. I have also written a book about programming R.

Related: Python
- Data Science