Interpretation of Confidence Levels

  • This is the description and instructions for the One-Dimensional Random Walk applet. This Applet relates random coin-flipping to random motion. It strives to show that randomness (coin-flipping) leads to some sort of predictable outcome (the bell-shaped curve).
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  • This is the description and instructions for the Two-Dimensional Random Walk applet. This Applet relates random coin-flipping to random motion but in more than one direction (dimension). It covers mean squared distance in the discussion.
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  • This is the description and instructions for the the Anthill and Molecular Motion applet. Topics include mixing, diffusion, and contour plots.
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  • This site provides the description and instructions for as well as the link to the Diffusion Limited Aggregation: Growing Fractal Structures applet. This applet strives to describe, classify, and measure different random fractal patterns in nature.
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  • This site provides the description and instructions for as well as the link to The Self-Avoiding Random Walk applet. In the SAW applet, random walks start on a square lattice and then are discarded as soon as they self-intersect. If a random walk survives after N steps, we compute the square of the distance from the origin, sum it up, and divide by the number of survivals. This variable is plotted on the vertical axis of the graph, which is plotted to the right of the field where random walks travel.
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  • This is the description and instructions as well as a link for the Forest Fires and Percolation applet. It builds a background with a "hands-on" activity for the students which then leads to the applet itself. The applet is a game where the object is to save as many trees from the forest fire as possible. It shows the spread of a fire with the variable of density and the probabilty of the number of surviving trees.
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  • This section on Common Statistical Tests uses an example on faculty publications to show users how to perform a one-sample t test. The discussion includes one-tailed and two-tailed tests.
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  • This page will calculate the lower and upper limits of the 95% confidence interval for a proportion, according to two methods described by Robert Newcombe, both derived from a procedure outlined by E. B. Wilson in 1927. The first method uses the Wilson procedure without a correction for continuity; the second uses the Wilson procedure with a correction for continuity.

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  • This resource gives a thorough definition of confidence intervals. It shows the user how to compute a confidence interval and how to interpret them. It goes into detail on how to construct a confidence interval for the difference between means, correlations, and proportions. It also gives a detailed explanation of Pearson's correlation. It also includes exercises for the user.

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  • This resource assists the user in reading, constructing, and understanding confidence intervals.
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