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  • This site gives a definition and an example of categorical data. Topics include two-way tables, bar graphs, and segmented bar graphs.
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  • This site gives an explanation, a definition and an example of inference in linear regression. Topics include confidence intervals for intercept and slope, significance tests, mean response, and prediction intervals.
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  • This site gives an explanation, a definition and an example of multiple linear regression. Topics include confidence intervals, tests of significance, and squared multiple correlation.
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  • This site gives an explanation, a definition and an example of ANOVA for regression. Topics include analysis of variance calculations for simple and multiple regression, and F-statistics.
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  • A good resource for problems in statistics in engineering. Contains some applets, and good textual examples related to engineering. Some topics include Monte Carlo method, Central Limit Theorem, Risk, Logistic Regression, Generalized Linear .Models, and Confidence.
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  • Discusses Markov chains, transition probabilities, and the transition probability matrix.
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  • Discusses the benefits of Taguchi methods applied to engineering.
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  • The user is be able to change the mean and the standard deviation using the sliders and see the density change graphically. The check buttons (68, 95, 99) will help one realize the appropriate percentages of the area under the curve. An example of thiis "68-95-99.7" rule follows.
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  • This is a basic web application that allows practice with matching points on a scatterplot to the appropriate correlation coefficient, r. Applet provides four scatterplots to match with four numeric correlations via radio buttons. After making selections, students click to see "correct" answers and keep a running total of proportion of correct matches, then may select four more plots.
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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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