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HyperStat Online: An Introductory Statistics Textbook and Online Tutorial for Help in Statistics Courses

HyperStat Online: An Introductory Statistics Textbook and Online Tutorial for Help in Statistics Courses
Recommend HyperStat to your friends on Facebook Click here for more cartoons by Ben Shabad. Other Sources Stat Primer by Bud Gerstman of San Jose State University Statistical forecasting notes by Robert Nau of Duke University related: RegressIt Excel add-in by Robert Nau CADDIS Volume 4: Data Analysis (EPA) The little handbook of statistical practice by Gerard E. Stat Trek Tutorial Statistics at square 1 by T. Concepts and applications of inferential statistics by Richard Lowry of Vassar College CAST by W. SticiGui by P. SurfStat by Keith Dear of the University of Newcastle. Introductory statistics: Concepts, models, and applications by David W. Multivariate statistics: Concepts, models, and applications by David W. Electronic textbook by StatSoft A new view of statistics by Will Hopkins of the University of Otago The knowledge base: An online research methods textbook by William M.

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Online Statistics Book: A Free Resource for Introductory Statistics Developed by Rice University (Lead Developer), University of Houston Clear Lake, and Tufts University OnlineStatBook Project Home This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. LeaRning Path on R - Step by Step Guide to Learn Data Science on R One of the common problems people face in learning R is lack of a structured path. They don’t know, from where to start, how to proceed, which track to choose? Though, there is an overload of good free resources available on the Internet, this could be overwhelming as well as confusing at the same time. To create this R learning path, Analytics Vidhya and DataCamp sat together and selected a comprehensive set of resources to help you learn R from scratch. This learning path is a great introduction for anyone new to data science or R, and if you are a more experienced R user you will be updated on some of the latest advancements.

StatPrimer © B. Gerstman 2003 StatPrimer (Version 6.4) B. Burt Gerstman (email) Part A (Introductory) The Athletics Statistics Page This page contains links to World Wide Web pages and other information concerning the statistics of the sport of Athletics (also known, in the United States, as Track and Field). Please send comments to: Jed Brickner My very own World Records for Lanes E-Mail Directory of Athletics Statisticians and Interesting Others List of Athletics Publications Athletics Photographs The T-and-F List Other Athletics Statistics Links Athletics Home Page from Petri Niininen. Probability and statistics EBook - Socr From Socr SOCR Books: 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.

Complimentary Exam Copies Please read the terms and conditions carefully before completing the form. To ensure that your request is processed quickly, please complete the required fields in the form below. eInspections – Save Time & The Environment Alternatively we can now offer many of our titles for inspection online. If you would like to inspect our books via this innovative, student-friendly format, simply select this option in the following form, give your full course details and, upon authorization, you will then be able to view the book from any PC with Internet access.

Introduction to Data Science with R - O’Reilly Media Learn practical skills for visualizing, transforming, and modeling data in R. This comprehensive video course shows you how to explore and understand data, as well as how to build linear and non-linear models in the R language and environment. It’s ideal whether you’re a non-programmer with no data science experience, or a data scientist switching to R from other software such as SAS or Excel. RStudio Master Instructor Garrett Grolemund covers the three skill sets of data science: computer programming (with R), manipulating data sets (including loading, cleaning, and visualizing data), and modeling data with statistical methods. Trefethen numerical ODE/PDE textbook Finite Difference and Spectral Methods for Ordinary and Partial Differential Equations Lloyd N. Trefethen Available online -- see below

Gallery of Data Visualization This Gallery of Data Visualization displays some examples of the Best and Worst of Statistical Graphics, with the view that the contrast may be useful, inform current practice, and provide some pointers to both historical and current work. We go from what is arguably the best statistical graphic ever drawn, to the current record-holder for the worst. Like good writing, good graphical displays of data communicate ideas with clarity, precision, and efficiency. ModernDive Getting Started - For Students This book was written using the bookdown R package from Yihui Xie (Xie 2016). In order to follow along and run the code in this book on your own, you’ll need to have access to R and RStudio. You can find more information on both of these with a simple Google search for “R” and for “RStudio.”

10 Stats Terms Explained in “Plain English” (#10: Standard Deviation) In the strictest sense, APA style discourages the use of color in graphics, stipulating that it be used only when it is "absolutely necessary". Consequently, most universities and dissertation committees also discourage (or downright forbid) the use of color graphics in dissertation manuscripts. Personally, i find this irritating, as I think most graphical representations of data can be made more clear with the appropriate use of color. Weka 3 - Data Mining with Open Source Machine Learning Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this.

Matlab Matlab is a tool for doing numerical computations with matrices and vectors. It can also display information graphically. The best way to learn what Matlab can do is to work through some examples at the computer. Connecting Math to Our Lives Connecting Math to Our Lives In this project students are invited to a) explore how math is used in their families and communities; and b) use math skills to investigate community or social concerns and then take action to promote greater equity in the world around them. Possible project/classroom activities: a) "What Math Means to Me" (Product: A math collage to share with a partner class)

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