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  • Playful song has recurrent mention of the term heteroscedasticity.
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  • Song describes concepts and steps of hypothesis testing. May be sung to the tune of "Mr. Bojangles" (Jerry Jeff Walker). Musical accompaniment realization and vocals are by Joshua Lintz from University of Texas at El Paso.
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  • Song is simply a quick jingle to help students recall the conceptual interpretation of a standard score (or, z-score), which in turn should help make sense of the formula. May be sung to tune of "Row, Row, Row Your Boat". Recorded June 26, 2009 at the OSU Whisper Room: Larry Lesser, vocals/guitar; Justin Slauson, engineer.
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  • Song invokes tradeoffs and pitfalls of there being (at least) two kinds of average: mean and median. May be sung to tune of "Love and Marriage" (Sammy Cahn and Jimmy Van Heusen). Musical accompaniment realization and vocals are by Joshua Lintz from University of Texas at El Paso.
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  • A cartoon that can be used in teaching about summary statistics. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.
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  • A quick pun about modeling and examining lack of fit by Bruce White
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  • This recording of a web seminar (webinar) provides a tour of the Assessment Resource Tools for Improving Statistical Thinking (ARTIST) web site. During this webinar, ARTIST team member Bob delMas guides you through the ARTIST website. The tour includes an overview of an online collection of literature on assessment in statistics education, much of which can be accessed online or downloaded. Resources for creating alternative forms of assessment such as student projects are also presented. You will also learn about efficient ways to create assessments from items from the ARTIST Item Database using a tool known as the Assessment Builder. By the end of the session, you will have learned how to select assessment items and download them in a format that can be edited with a word processor.
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  • A quick pun about the "log scale" by Bruce White
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  • This Flash based applet simulates data from a case study of treatments for tumor growth in mice. This simulation allows the user to place mice into a control and treatment groups. The simulation then compares the difference in the groups based on this haphazard selection to those of a truly random assignment (the user may also create multiple random assignments and examine the sampling distribution of key statistics). The applet may be used to illustrate three points about random assignment in experiments: 1) how it helps to eliminate bias when compared with a haphazard assignment process, 2) how it leads to a consistent pattern of results when repeated, and 3) how it makes the question of statistical significance interesting since differences between groups are either from treatment or by the luck of the draw. In this webinar, the activity is demonstrated along with a discussion of goals, context, background materials, class handouts, and assessments. Key Note for Instructors: The data are drawn from a real experiment with an effective treatment but where the response is correlated with animal age and size (so tumor size will tend to be smaller in the treatment group when measured at the end of a randomized experiment but animal age and size should not be). Typically people choosing haphazardly will tend to pick larger/older animals for the treatment group and thus create a bias against the treatment.
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  • A quick pun about "autocorrelation" by Bruce White.
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