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One Numerical Variable

  • A song describing how sample means will follow the normal curve regardless of how skewed the population histogram is, provided n is very large.

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  • A cartoon to teach how it is important to look at variation, not just averages. 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.
    4.5
    Average: 4.5 (2 votes)
  • A cartoon for teaching about the interpretation of basic 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.
    4
    Average: 4 (1 vote)
  • A cartoon to teach about using boxplots to summarize a distribution. 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 cartoon to teach about interpreting observational studies. 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.
    1
    Average: 1 (1 vote)
  • A cartoon to teach about the importance of diagnostics in Markoc chain Monte Carlo procedures. 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 cartoon to teach about comparing parametric versus non-parametric inference. Cartoon by John Landers (www.landers.co.uk) based on an idea from "Lower Bounds on Statistical Humor" by Alan H. Feiveson, Mark Eakin, and Richard Alldredge. Free to use in the classroom and on course web sites.
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  • This tutorial on the Kruskal-Wallis test includes its definition, assumptions, characteristics, and hypotheses as well as procedures for graphical comparisons. An example using output from the WINKS software is given, but those without the software can still use the tutorial. An exercise is given at the end that can be done with any statistical software package.
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  • Using cooperative learning methods, this lesson introduces distributions for univariate data, emphasizing how distributions help us visualize central tendencies and variability. Students collect real data on head circumference and hand span, then describe the distributions in terms of shape, center, and spread. The lesson moves from informal to more technically appropriate descriptions of distributions.
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  • Using cooperative learning methods, this activity helps students develop a better intuitive understanding of what is meant by variability in statistics. Emphasis is placed on the standard deviation as a measure of variability. This lesson also helps students to discover that the standard deviation is a measure of the density of values about the mean of a distribution. As such, students become more aware of how clusters, gaps, and extreme values affect the standard deviation.
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