Non-symbolic math

  • This tutorial on the Mann-Whitney test includes its definition, assumptions, characteristics, and hypotheses as well as procedures for graphical comparisons. An example using output from the WINKS software is given, but those without the software can still use the tutorial. An exercise is given at the end that can be done with any statistical software package.
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  • This glossary gives definitions for numerous statistical terms, concepts, methods, and rules.
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  • This tutorial opens with a survey on polling. Upon completing the survey, students are taken through an election example which uses polling to explain random sampling, bias, margin of error, and confidence intervals.
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  • This random number service allows users to generate up to 10,000 random integers with duplicates, randomized sequences without duplicates, or up to 16 kilobytes of raw random bytes. Users can also flip virtual coins and generate random bitmaps. Key word: Random Number Generator.

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  • HotBits is a genuine random number generator powered by radioactive decay. Simply click the "Request HotBits" link, and specify how many bytes you would like (up to 2048) and in what form you prefer them. Hexadecimal returns numbers and letters, while C language returns integers. Then click the "Get HotBits" button, and your random numbers will appear on the screen.
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  • This resource explains Multiple Regression and concepts associated with it. Key Words: Predicted values; Residuals; Dummy Variables; Interaction Effects; T-Test; Regression Coefficients; Correlation; Partial Correlation; R-Squared; Adjusted R-Squared; Multicollinearity; Variance-Inflation Factors; Transformation; Cook's Distance; Validity; Durbin-Watson Coefficient.
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  • The datasets in this collection are in text format, but are also compatible with Arc software from "Regression Graphics." Each set has a title, description, and data table. The software is available in the relation link below.
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  • This collection of datasets, posted by UCLA, is divided into 6 groups: Datasets for Teaching; Data from Books; Data from Consulting Projects; Data from National Statistics Agencies; Social Science Data Archives; Data from US Governmental Agencies. The data from books come from the following authors: Petruccelli, Nandram and Chen; Freedman, Pisani, and Purves; Andrews and Herzberg; Carlson and Thorn; Cox and Snell; Hand, Daly, Lunn, McConway and Ostrowski; and Moore.
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  • This site presents may articles on current events and issues that challenge statistics reported in the news. Each article encourages readers to think critically about statistics reported by the media and to look at the whole picture before believing conclusions presented in the news. "Our goals are to correct scientific misinformation in the media resulting from bad science, politics, or a simple lack of information or knowledge."
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  • This Flash based applet simulates data from a case study of treatments for tumor growth in mice. This simulation allows the user to place mice into a control and treatment groups. The simulation then compares the difference in the groups based on this haphazard selection to those of a truly random assignment (the user may also create multiple random assignments and examine the sampling distribution of key statistics). The applet may be used to illustrate three points about random assignment in experiments: 1) how it helps to eliminate bias when compared with a haphazard assignment process, 2) how it leads to a consistent pattern of results when repeated, and 3) how it makes the question of statistical significance interesting since differences between groups are either from treatment or by the luck of the draw. In this webinar, the activity is demonstrated along with a discussion of goals, context, background materials, class handouts, and assessments. Key Note for Instructors: The data are drawn from a real experiment with an effective treatment but where the response is correlated with animal age and size (so tumor size will tend to be smaller in the treatment group when measured at the end of a randomized experiment but animal age and size should not be). Typically people choosing haphazardly will tend to pick larger/older animals for the treatment group and thus create a bias against the treatment.
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