ONLINE OPEN-ACCESS TEXTBOOKS Search form You are here Forecasting: principles and practice Rob J Hyndman George Athanasopoulos Statistical foundations of machine learning Gianluca Bontempi Souhaib Ben Taieb Electric load forecasting: fundamentals and best practices Tao Hong David A. Modal logic of strict necessity and possibility Evgeni Latinov Applied biostatistical analysis using R Stephen B. Introduction to Computing : Explorations in Language, Logic, and Machines David Evans Over 100 Incredible Infographic Tools and Resources (Categorized) - DailyTekk This post is #6 in DailyTekk’s famous Top 100 series which explores the best startups, gadgets, apps, websites and services in a given category. Total items listed: 112. Time to compile: 8+ hours. Update: Be sure to check out our latest post on infographics: Infographics Are Everywhere – Here’s How to Make Yours Go Viral. I love a good infographic! You might also like: Post Navigation The Best Blogs and Websites about Infographics Visual.ly – Awesome community for creating and sharing infographics.Information Aesthetics – The relationship between design and information.Visualizing.org – Making sense of complex issues through data and design.Visual Complexity – A resource for the visualization of complex networks.Daily Infographic – A new infographic every day.GOOD Infographics – GOOD Magazine’s excellent infographics section.Information Is Beautiful – Ideas, issues, knowledge, data – visualized.Infographic of the Day – ... There’s more to this article!
Probability and statistics EBook - Socr From Socr This is a General Statistics Curriculum E-Book, which includes Advanced-Placement (AP) materials. Preface This is an Internet-based probability and statistics E-Book. The materials, tools and demonstrations presented in this E-Book would be very useful for advanced-placement (AP) statistics educational curriculum. The E-Book is initially developed by the UCLA Statistics Online Computational Resource (SOCR). There are 4 novel features of this specific Statistics EBook. Format Follow the instructions in this page to expand, revise or improve the materials in this E-Book. Learning and Instructional Usage This section describes the means of traversing, searching, discovering and utilizing the SOCR Statistics EBook resources in both formal and informal learning setting. Copyrights The Probability and Statistics EBook is a freely and openly accessible electronic book developed by SOCR and the general community. Chapter I: Introduction to Statistics The Nature of Data and Variation Counting
Mining of Massive Datasets The book has now been published by Cambridge University Press. The publisher is offering a 20% discount to anyone who buys the hardcopy Here. By agreement with the publisher, you can still download it free from this page. Cambridge Press does, however, retain copyright on the work, and we expect that you will obtain their permission and acknowledge our authorship if you republish parts or all of it. --- Jure Leskovec, Anand Rajaraman (@anand_raj), and Jeff Ullman Download Version 2.1 The following is the second edition of the book, which we expect to be published soon. There is a revised Chapter 2 that treats map-reduce programming in a manner closer to how it is used in practice, rather than how it was described in the original paper. Version 2.1 adds Section 10.5 on finding overlapping communities in social graphs. Download the Latest Book (511 pages, approximately 3MB) Download chapters of the book: Download Version 1.0 Download the Book as Published (340 pages, approximately 2MB)
Glossary / Mathematics and statistics Statistics This glossary describes terms used in the achievement objectives of the statistics strand of the curriculum, as well as other related terms. Many of these terms have other meanings when used in other contexts. Terms in this glossary that appear in another description are italicised when they are used for the first time. In descriptions of terms from the probability thread, events are in bold. Some terms have equivalent names listed under ‘Alternative’, and closely related terms are listed under ‘See’. The terms provide references to levels in the statistics strand achievement objectives. Download the full glossary of terms:
Gapminder: Unveiling the beauty of statistics for a fact based world view. An R "meta" book by Joseph Rickert I am a book person. I collect books on all sorts of subjects that interest me and consequently I have a fairly extensive collection of R books, many of which I find to be of great value. Recently, however, while crawling around CRAN, it occurred to me that there is a tremendous amount of high quality material on a wide range of topics in the Contributed Documentation page that would make a perfect introduction to all sorts of people coming to R. The content column lists the topics that I think ought to be included in a good introductory probability and statistics textbook. Finally, I don’t mean to imply that the documents in my table are the best assembled in the Contributed Documentation page.
It is so random! Or is it? The meaning of randomness First there is the problem of lexical ambiguity. There are colloquial meanings for random that don’t totally tie in with the technical or domain-specific meanings for random. Then there is the fact that people can’t actually be random. Then there is the problem of equal chance vs displaying a long-term distribution. And there is the problem that there are several conflicting ideas associated with the word “random”. In this post I will look at these issues, and ask some questions about how we can better teach students about randomness and random sampling. Lexical ambiguity Representations of the different meanings of the word, random. So what are the different meanings for random? Different meanings Without method The first meaning of random describes something happening without pattern, method or conscious decision. Statistical meaning Most on-line dictionaries also give a statistical definition, which includes that each item has an equal probability of being chosen. Informal or colloquial
Thesis: practical tools for exploring data and models Practical tools for exploring data and models This thesis describes three families of tools for exploring data and models. It is organised in roughly the same way that you perform a data analysis. Download (1.8 meg, pdf) Buy online (122 pages, $25 + shipping) Watch model visualisation videos R packages Reshaping data with the reshape package: reshape A layered grammar of graphics: ggplot2 Visualisation models: classifly, clusterfly, meifly Seminar Slides (printer friendly) Teaching statistical language I received a phone call from the company that leases us our equipment. I got quite excited when the salesman told me they would waive the purchase price of a new iPad. Then I decided it was time to clarify things. “Ok,” I said, “You are using the term ‘purchase price’. I didn’t have it right. Sadly. He was using the term “purchase price” to describe an extra payment at the end of the two-year term to allow me to “purchase” the two-year-old iPad. Similar confusion arises when common terms are used in specialized ways within a discipline. “Significant”, “Random, “Regression” and “Normal” have common meanings quite distinct from their technical meaning. Sometimes, like my friendly salesman, we can forget how confusing these terms are. In writing this post I found numerous references to this problem, and explanations of tricky terms. Here are some thoughts on how to address the problem when teaching. Be aware Be explicit Use a modifier if it makes sense Gloss Assess for it Provide examples
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