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Statistical Topic

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  • This resource gives a thorough definition of confidence intervals. It shows the user how to compute a confidence interval and how to interpret them. It goes into detail on how to construct a confidence interval for the difference between means, correlations, and proportions. It also gives a detailed explanation of Pearson's correlation. It also includes exercises for the user.

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  • JFreeReport is a free Java report library. It has the following features: full on-screen print preview; data obtained via Swing's TableModel interface (making it easy to print data directly from your application); XML-based report definitions; output to the screen, printer or various export formats (PDF, HTML, CSV, Excel, plain text); support for servlets (uses the JFreeReport extensions) complete source code included (subject to the GNU Lesser General Public Licence); extensive source code documentation.

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  • This resource defines and explains Chi square. It takes the user through 5 different categories: 1) Testing differences between p and pi 2) More than two categories 3) Chi-square test of independence 4) Reporting results 5) Exercises.

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  • This chapter of the HyperStat Online Textbook discusses in detail sampling distributions of various statistics (mean, median, proportions, correlation, etc.), differences between such statistics, the Central Limit Theorem, and standard error, giving formulas, examples, and exercises.

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  • This site defines power and explains what factors may affect it, such as significance level, sample size and variance.

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  • These handouts/links give a foundational understanding of how to set up and use R

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  • These cheat sheets make it easy to learn about and use some of the favorite packages of RStudio. 

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  • This compendium facilitates the creation of good graphs by presenting a set of concrete examples, ranging from the trivial to the advanced. The graphs can all be reproduced and adjusted by copy-pasting code into the R console. Almost every example in this compendium is driven by the same philosophy: A good graph is a simple graph, in the Einsteinian sense that a graph should be made as simple as possible, but not simpler.  A note for R fans: the majority of our plots have been created in base R, but you will encounter some examples in ggplot.

     

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  • As our economy, society, and daily life become increasingly dependent on data, work across nearly all fields is becoming more data driven, affecting both the jobs that are available and the skills that are required. At the request of the National Science Foundation, the National Academies of Sciences, Engineering, and Medicine were asked to set forth a vision for the emerging discipline of data science at the undergraduate level. The study committee considered the core principles and skills undergraduates should learn and discussed the pedagogical issues that must be addressed to build effective data science education programs. Data Science for Undergraduates: Opportunities and Options underscores the importance of preparing undergraduates for a data-enabled world and recommends that academic institutions and other stakeholders take steps to meet the evolving data science needs of students. 

     

    Watch the report release webinar here:  https://vimeo.com/269033724  

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  • The app allows you to see the trade-offs on various types of outlier/anomaly detection algorithms. Outliers are marked with a star and cluster centers with an X.

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