General Rules

  • This dataset comes from a study on two treatment sequences (AB, BA) given to 14 healthy male volunteers randomly assigned to a two-period crossover design. Three pharmacokinetic variables were collected on the subjects at the end of each treatment period. Questions this study focused on refer to whether the treatments (A,B)are equivalent. A text file version of the data is found in the relation link.
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  • This dataset comes from a study on drug treatments of reflux disease patients. Twelve patients were assigned to a four period crossover design, and data on their disease symptoms were collected after treatment. Questions this study focused on refer to dosage of the drug. A text file version of the data is found in the relation link.
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  • This module discusses the probability of an event and relative frequency. The applet shows how empirical probability converges to theoretical probability as the sample size increases. The follow-up example includes an applet that simulates drawing differently colored balls from an urn.
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  • This poem, by North Carolina State University Emeritus Professor of Physics Jasper D. Memory (1935 - ) is designed to teach the difference between the probability of having a disease given a positive screening test and the probability of a positive test result given you have the disease. The poem was published in the October, 2007 issue of "Mathematics Magazine" volume 80 p. 273,
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  • Song describes conditions for using the t distribution and mentions its inventor William Gosset (and his pseudonym, Student). May be sung to the tune of "Let it Be" (McCartney/Beatles). Musical accompaniment realization and vocals are by Joshua Lintz from University of Texas at El Paso.
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  • This article, in a series, describes a game, which tests opposing strategies through aspects of experiemental design.
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  • This site provides an outline of an activity for introducing Bayes' Theorem and conditional probability.
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  • The BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. This site is primarily concerned with the stand-alone WinBUGS 1.4.1 package, which has a graphical user interface and on-line monitoring and convergence diagnostics. This program can be downloaded for free from the site.

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  • This site provides applets, lessons, and objectives for learning about conditional probability. The applet activity introduces multiple-outcomes events and computing probabilities.
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  • This is a collection of activities as Java applets that can be used to explore probability and statistics. Each activity is supplemented with background information, activity instructions, and a curriculum for the activity.
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