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  • A song about examining the assumptions in statistical procedures especially dealing with skewed distributions. The lyrics were written by Robert Carver of Stonehill College and were awarded second place in the song category of the 2011 CAUSE A-Mu-sing competition. The song is a parody of the 1961 classic pop song "Runaround Sue" written by Ernie Maresca and Dion DiMucci and sung by Dion backed by the vocal group, The Del-Satins. Musical accompaniment realization and vocals are by Joshua Lintz from University of Texas at El Paso.

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  • Three Haiku related to regression including the topics of checking assumptions, dealing with non-linear patterns, and partitioning sums of squares. The Haiku were written by Elizabeth Stasny of The Ohio State University and were awarded a tie for second place in the poetry category of the 2011 CAUSE A-Mu-sing competition.

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  • In this 20 minute video, doctor and researcher Hans Rosling uses his fascinating data-bubble software to burst myths about the developing world. The video includes new analysis on China and the post-bailout world, mixed with classic data shows.

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  • In this short 3 minute video, mathematician and magician Arthur Benjamin offers a bold proposal on how to make math education relevant in the digital age.

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  • A poem useful in teaching aspects about hypothesis testing, especially the caveat that unimportant differences may be deemed significant with a large sample size. The poem was written by Mariam Hermiz, a student at University of Toronto, Mississauga in Fall 2010 as part of an assignment in a biometrics class taught by Helene Wagner. The poem was awarded first place in the poetry category of the 2011 CAUSE A-Mu-sing contest.

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data.  This lecture overs the following: covariance patterns and generalized estimating equations (GEE). 

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data.  This lecture overs the following: conditional logistic regression, conditional likelihood for matched pairs, the non-central hypergeometric, the conditional maximum likelihood estimator (CMLE), conditional confidence interval for odds ratios, and McNemar's statistic.

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data.  This lecture overs the following:  odds ratio, dependent proportion, marginal homogeneity, McNemar's Test, marginal homogeneity for greater than 2 levels, measures of agreement, and the kappa coefficient (weighted vs. unweighted).

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: sparse tables, sampling zeros, structural zeros, and log-linear model (and limitations).

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: partial/conditional tables, confounding, types of independence (mutual, joint, marginal, and conditional), identifiability constraints, partial odds ratios, hierarchical log-linear model, pairwise interaction log-linear model, conditional independence log-linear model, goodness of fit, and model building.

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