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Online Statistics Education: A Free Resource for Introductory Statistics

Online Statistics Education: 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. Please cite as: Online Statistics Education: A Multimedia Course of Study ( Project Leader: David M. If you are an instructor using these materials in a class we would appreciate hearing from you. Version in PDFe-Pub (e-book)Interactive e-book (for IOS and OS X) Table of Contents Mobile This version uses formatting that works better for mobile devices. Rice Virtual Lab in Statistics This is the original classic with all the simulations and case studies. Version in PDF e-Pub (e-book) Partial support for this work was provided by the National Science Foundation's Division of Undergraduate Education through grants DUE-9751307, DUE-0089435, and DUE-0919818.

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Rice Virtual Lab in Statistics (RVLS) Partial support for this work was provided by the National Science Foundation's Division of Undergraduate Education through grant DUE 9751307. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Last updated: 12/3/108 Permissions Permission is granted to link to any portion of the Rice Virtual Lab. The applets in the simulations/demonstrations are hereby in the public domain and can therefore be used without restriction. 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

50+ Data Science and Machine Learning Cheat Sheets Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms. Cheatsheets on Python, R and Numpy, Scipy, Pandas There are thousands of packages and hundreds of functions out there in the Data science world! An aspiring data enthusiast need not know all. Here are the most important ones that have been brainstormed and captured in a compact few pages. Mastering Data science involves understanding of statistics, Mathematics, Programming knowledge especially in R, Python & SQL and then deploying a combination of all these to derive insights using the business understanding & a human instinct—that drives decisions.

Pearson Product-Moment Correlation - When you should run this test, the range of values the coefficient can take and how to measure strength of association. What does this test do? The Pearson product-moment correlation coefficient (or Pearson correlation coefficient, for short) is a measure of the strength of a linear association between two variables and is denoted by r. Basically, a Pearson product-moment correlation attempts to draw a line of best fit through the data of two variables, and the Pearson correlation coefficient, r, indicates how far away all these data points are to this line of best fit (i.e., how well the data points fit this new model/line of best fit). What values can the Pearson correlation coefficient take? The Pearson correlation coefficient, r, can take a range of values from +1 to -1. A value of 0 indicates that there is no association between the two variables.

Statistical Power « PreviousHomeNext » There are four interrelated components that influence the conclusions you might reach from a statistical test in a research project. The logic of statistical inference with respect to these components is often difficult to understand and explain. This paper attempts to clarify the four components and describe their interrelationships. The four components are: 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. Furthermore you will be able to annotate and highlight parts of the book and share the online edition with your colleagues. Terms and Conditions

Academic Phrasebank – Referring to Sources One of the distinguishing features of academic writing is that it is informed by what is already known, what work has been done before, and/or what ideas and models have already been developed. Thus, in academic texts, writers frequently make reference to other studies and to the work of other authors. It is important that writers guide their readers through this literature. This section of Academic Phrasebank lists some of the phrases that writers may use for this purpose. A note on the literature review: It is the purpose of the literature review section of a paper or dissertation to show the reader, in a systematic way, what is already known about the research topic as a whole, and to outline the key ideas and theories that help us to understand this.

Beginner's guide to R: Introduction R is hot. Whether measured by more than 4,400 add-on packages, the 18,000+ members of LinkedIn's R group or the close to 80 R Meetup groups currently in existence, there can be little doubt that interest in the R statistics language, especially for data analysis, is soaring. Why R? It's free, open source, powerful and highly extensible. "You have a lot of prepackaged stuff that's already available, so you're standing on the shoulders of giants," Google's chief economist told The New York Times back in 2009.

Improving Conclusion Validity « PreviousHomeNext » Improving Conclusion Validity So you may have a problem assuring that you are reaching credible conclusions about relationships in your data. 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. However, I suppose the guideline is meant to provide uniformity and consistency across manuscripts, which is understandable. Unfortunately, if you use SPSS you've probably already discovered that it produces graphics in color by default.

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