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Variable Classification

  • This course covers methodology, major software tools and applications in data mining. By introducing principal ideas in statistical learning, the course will help students to understand conceptual underpinnings of methods in data mining. It focuses more on usage of existing software packages (mainly in R) than developing the algorithms by the students. The topics include statistical learning; resampling methods; linear regression; variable selection; regression shrinkage; dimension reduction; non-linear methods; logistic regression, discriminant analysis; nearest-neighbors; decision trees; bagging; boosting; support vector machines; principal components analysis; clustering. Perfect for students and teachers wanting to learn/acquire materials for this topic.

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  • A song for use in helping students to learn the four levels of measurement (nominal, ordinal, interval, ratio) in appropriate hierarchical order and to identify examples of each in context.  Lyrics by Larry Lesser and music by Larry Lesser and Dominic Dousa copyright 2015.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

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  • A song for teaching concepts of estimating a population mean and addressing uncertainty in the estimate. The lyrics were written by Lawrence Mark Lesser from University of Texas at El Paso as a parody of the 2011 song "Call Me Maybe" written by Carly Rae Jepsen, Tavish Crowe, and Josh Ramsay). The lyrics were awarded second prize in the 2013 CAUSE A-Mu-sing competition. Free for non-profit educational use. Musical accompaniment realization are by Joshua Lintz and vocals are by Mariana Sandoval from University of Texas at El Paso.

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  • A joke that can be used when teaching six sigma process control ideas or chi-squared goodness-of-fit tests. The joke was written in 2013.

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  • A poem to teach about various types of variables (categorical versus numerical versus summary statistics) and differentiating them from other concepts like the outcomes in the sample space or the sample size. The poem was composed by Lawrence Mark Lesser of The University of Texas at El Paso.
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  • The purpose of this applet is to provide students with guided practice through problems on hypothesis testing for a population proportion using the method of rejection regions.
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  • This issue contains articles about microarray data and the partnership between statisticians and biologists, ASA Stat Bowl at JSM 2005, an interview with Stat Bowl 2004 champion Jesse Frey, USCOTS 2005 plans, cluster sampling, an analysis of Civil War intelligence sleuth's Alan Pinkerton's incompetence.
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  • This issue contains articles about the birthday problem probabilities using simulation analysis using R; making money on eBay using multiple regression to estimate prices of violins; McDonald's French fry actual mass vs. industry standard mass student project; PC vs. Mac computers survey of Harvard students; EESEE electronic story and exercise encyclopedia; 12 types of variables used in statistical analysis; the history of probability in the Enlightenment for rational decisions in law, science, and politics.
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  • This issue contains articles about statistics in sports, including batting average, using scatterplots to predict the winners of long-distance races, regression analysis and the NFL, determining the greatest cyclist ever, simulation in public opinion polls, and determining the "best" athletes for cycling and baseball.
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  • This issue contains articles about binomial confidence intervals; the team effect in stock car racing; using multiple tests (one-sample t-test and sign test); the "two-envelope exchange paradox" (similar to the Monty Hall problem) with discussions of expectation, likelihood, and inference; regression line vs. trend line; calculations of standard normal table values and pi; teaching at a small liberal arts college; modeling extreme events.
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