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  • Data sets were submitted by the authors of articles in JSE. Each data set is presented along with a link to the article that references the data.
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  • "The purpose of this electronic service is to provide access to a collection of datasets suitable for teaching statistics. The datasets are stored either locally or on other computers throughout the world. The datasets have been organized by statistical technique to make it easier for you to find a dataset appropriate for your pedagogical needs. When a dataset is appropriate for several statistical techniques, it will appear under several categories. Each dataset consists of three files: one is a description of the data; the others are an ascii (text) file of the data and an Excel file of the data."
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  • Many data sets useful for modeling bivariate relationships. The data sets are formatted for use in Fathom, but text versions are also available.
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  • This site presents 19 videos of statisticians summarizing a project that they did. Each video is accompanied by a dataset so that viewers can try to recreate the statistics in the video. Video runtimes vary from about 8 minutes to as many as 35 minutes.
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  • This is a collection of data sets that were part of R packages. The data set page includes information on which package the data set comes from, the name of the data set, and the number of rows and columns included. Each set is given in .csv form with a documentation file also.
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  • This collection of datasets from Dr. John Rasp's Statistics Webpage is for his STAT 460 (Experimental Design & Advanced Data Analysis), STAT 301 (Business Statistics), STAT 201 (Intro to Business Statistics) classes. This also provides links for statistical web pages, resources for statistical studies, Homework and lecture reviews.
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  • Big data analysis is explained in this online course that introduces the user to the tools Hadoop and Mapreduce. These tools allow for the parallel computing necessary to analyze large amounts of data.

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  • This tutorial on SQL teaches the most used commands. There is a short explanation, then the user is asked a simple question. If the typed answer is correct, the user continues to the next lesson.
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  • This is a complete lesson module (including example problems with answers to selected problems) for the purpose of enabling students to: 1) Provide examples demonstrating how the margin of error, effect size, and variability of the outcome affect sample size computations. 2) Compute the sample size required to estimate population parameters with precision. 3) Interpret statistical power in tests of hypothesis. 4) Compute the sample size required to ensure high power when hypothesis testing.
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  • When performing a hypothesis test about the population mean, a possible reason for the failure of rejection of the null hypothesis is that there's an insufficient sample size to achieve a powerful test. Using a small data set, Minitab is used to check for normality of the data, to perform a 1-Sample t test, and to compute Power and Sample Size for 1-Sample t.

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