Application

  • OStats is a simple tool for data visualisation and statistical analysis, particularly aimed at helping students learn statistics.

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  • A cartoon for use in discussing Uniformly Most Powerful Tests. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.
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  • DataFerrett is a unique data analysis and extraction tool -- with recoding capabilities -- to customize federal, state, and local data to suit your requirements. Using DataFerrett, you can develop an unlimited array of customized spreadsheets that are as versatile and complex as your usage demands. The DataFerrett helps you locate and retrieve the data you need across the Internet to your desktop or system, regardless of where the data resides. You can then develop and customize tables. Selecting your results in your table you can create a chart or graph for a visual presentation into an html page. Save your data in the databasket and save your table for continued reuse. The DataFerrett is a Beta testing version that will incorporate the latest bug fixes, enhancements, and new functionality that will be rolled into the DataFerrett after testing has been completed.

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  • Beware of bugs in the above code; I have only proved it correct, not tried it. is a quote from American computer scientist Donald E. Knuth (1938 - ). The quote was written on March 22, 1977 as the last sentence of a five-page memo entitled "Notes on the van Emde Boas construction of priority deques: An instructive use of recursion."
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  • The BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. This site is primarily concerned with the stand-alone WinBUGS 1.4.1 package, which has a graphical user interface and on-line monitoring and convergence diagnostics. This program can be downloaded for free from the site.

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  • This program allows the student to explore the nature of sampling distributions of sample means and sample proportions. The software provides separate windows for building population distributions, drawing and viewing random samples from the population, exploring the behavior of sampling distributions of sample means, and exploring the behavior of confidence intervals.
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  • The Decision Bonsai are a hybrid of concept maps and decision trees. They were originally developed to give introductory statistics students a map to inference procedures but have evolved to be used for other topics. The tree is 'grown' during the semester so that students build a picture of the relationships in their mind. Recent work is moving toward the development of more complete concept maps for introductory statistics, statistical quality methods and probability and stochastic processes courses. These Decision Bonsai would be then pointed to at appropriate points in the concept maps.
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  • This lesson describes bootstrapping in the context of a statistics class for psychology students.
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  • This is an example of "growing" a decision tree to analyze two possible outcomes. The tree's branches examine the two possible conditions of employee drug use with corresponding probabilities. This example looks at the final outcome probabilities of being correctly and incorrectly identified versus testing accuracy.
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  • This general, introductory tutorial on mathematical modeling (in pdf format) is intended to provide an introduction to the correct analysis of data. It addresses, in an elementary way, those ideas that are important to the effort of distinguishing information from error. This distinction constitutes the central theme of the material described herein. Both deterministic modeling (univariate regression) as well as the (stochastic) modeling of random variables are considered, with emphasis on the latter. No attempt is made to cover every topic of relevance. Instead, attention is focussed on elucidating and illustrating core concepts as they apply to empirical data.

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