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Statistical Inference & Techniques

  • 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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  • A cartoon to aid in the discussion of the difference between descriptive and inferential statistics. The cartoon was created by Greg Crowther from Everett Community College and took second place in the cartoon category of the 2017 A-mu-sing competition.
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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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  • A short story that might be used as an out-of-class assignment to facilitate understanding the interpretation of a 95% confidence interval as a random interval that is expected to cover the true parameter in 95% of all samples. The story was written in 2011 by Canadian mathematician Robert Dawson from Saint Mary's University in Halifax Nova Scotia. The story was published as a two part series at www.Lablit.com
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  • A science fiction short story that could be used in an out-of-class assignment associated with the topic of cyclic trends in time series. The story was written in 1952 by American science fiction writer Robert Heinlein and published in Galaxy Science Fiction magazine.
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  • A joke that might be used in a discussion of the problem of using a simple linear regression to extrapolate beyond the range of the data (where it is unlikely that the linear relationship would continue to hold). The joke was written by Dennis Pearl from Penn State University.
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  • A cartoon to be used for discussing the selection of the best explanatory variable in a regression model. The cartoon was used in the March 2017 CAUSE Cartoon Caption Contest. The winning caption was submitted by Michele Balik-Meisner, a student at North Carolina State University. The drawing was created by British cartoonist John Landers based on an idea from Dennis Pearl of Penn State University. A second winning entry, by Michael Posner of Villanova University, may be found at www.causeweb.org/cause/resources/fun/cartoons/variable-wheel-ii Three honorable mentions that rose to the top of the judging in the March competition included “No no no! You randomize AFTER you select your research topic!” by Mickey Dunlap from University of Georgia; “This isn't what I meant by random variable!” by Larry Lesser from The University of Texas at El Paso; and “We find this method of finding 'significant' predictors to be quicker than using stepwise regression and it is even slightly more reproducible.” by Greg Snow from Brigham Young University.
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  • A cartoon to be used for discussing the selection of the best explanatory variable in a regression model. The cartoon was used in the March 2017 CAUSE Cartoon Caption Contest. The winning caption was submitted by Michael Posner, from Villanova University. The drawing was created by British cartoonist John Landers based on an idea from Dennis Pearl of Penn State University. A second winning entry, by Michele Balik-Meisner, a student at North Carolina State University, may be found at www.causeweb.org/cause/resources/fun/cartoons/variable-wheel-i Three honorable mentions that rose to the top of the judging in the March competition included “No no no! You randomize AFTER you select your research topic!” by Mickey Dunlap from University of Georgia; “This isn't what I meant by random variable!” by Larry Lesser from The University of Texas at El Paso; and “We find this method of finding 'significant' predictors to be quicker than using stepwise regression and it is even slightly more reproducible.” by Greg Snow from Brigham Young University.
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