Python Tutorials for Kids 8+ 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.
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.
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[edit] 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[edit] Larry Wasserman's book All of Statistics[6]The Statistics and the Econometric Theory wikibooks.The Econometrics and Statistics pages on wikipedia. References[edit]
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.
start [R Wiki] * R is a free software environment for statistical computing and graphics. It runs on a wide variety of UNIX platforms, Windows and MacOS. This R Wiki is dedicated to the collaborative writing of R documentation. For information on browsers, RSS syndication, copyright, ... read usage. R comes with several official manuals and FAQs. These should be your primary source of information. Personal note: I have some difficulties with the statement that the official manuals should be your primary source of information. Books I found ordinary books to be the most helpful source of information for novice programmers and I can particularly recommend: Adler, J. (2010) R in a nutshell. Help forum for programmers The R-Help mailing list can give excellent answers, but due to the high calibre of respondents it can be intimidating, (and off-putting). In addition I can’t but feel that many questions are a waste of time for most of those who respond. Stackoverflow
How to use R R is a powerful, free and open source, cross-platform, statistical and graphing software package;programming language;software environment for statistical computing. Downloading R[edit] Visit the R Project home page. Tutorials[edit] Books that are Helpful When Learning R[edit] See also[edit] External links[edit] Books[edit] 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
Quantitative Methods In Linguistics (9781405144254): Keith Johnson Big Data, Data Mining, Predictive Analytics, Statistics, StatSoft Electronic Textbook This free ebook has been provided as a public service since 1995. Statistics: Methods and Applications textbook offers training in the understanding and application of statistics and data mining. It covers a wide variety of applications, including laboratory research (biomedical, agricultural, etc.), business statistics, credit scoring, forecasting, social science statistics and survey research, data mining, engineering and quality control applications, and many others. The Textbook begins with an overview of the relevant elementary (pivotal) concepts and continues with a more in depth exploration of specific areas of statistics, organized by "modules", representing classes of analytic techniques. You have filtered out all documents.
Code Like a Pythonista: Idiomatic Python In this interactive tutorial, we'll cover many essential Python idioms and techniques in depth, adding immediately useful tools to your belt. There are 3 versions of this presentation: ©2006-2008, licensed under a Creative Commons Attribution/Share-Alike (BY-SA) license. My credentials: I am a resident of Montreal,father of two great kids, husband of one special woman,a full-time Python programmer,author of the Docutils project and reStructuredText,an editor of the Python Enhancement Proposals (or PEPs),an organizer of PyCon 2007, and chair of PyCon 2008,a member of the Python Software Foundation,a Director of the Foundation for the past year, and its Secretary. In the tutorial I presented at PyCon 2006 (called Text & Data Processing), I was surprised at the reaction to some techniques I used that I had thought were common knowledge. Many of you will have seen some of these techniques and idioms before. These are the guiding principles of Python, but are open to interpretation. import this
Analyzing Linguistic Data: A Practical Introduction to Statistics using R (9780521709187): R. H. Baayen