High School

  • This page will compute the Two-Way Factorial ANOVA for Independent Samples, for up to four rows by four columns. This page will also calculate the critical values of Tukey's HSD for purposes of post-ANOVA comparisons.

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  • Given a sample of N values of X randomly drawn from a normally distributed population, this page will calculate the .95 and .99 confidence intervals (CI) for the estimated mean of the population.

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  • This page will calculate the intercorrelations (r and r2) for up to five variables, designated as A, B, C, D, and E.

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  • This resource defines what a p-value is, why .05 is significant, and when to use it. It also covers related topics such as one-tailed/two-tailed tests and hypothesis testing.
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  • This resource defines a pie chart. It also allows the user to input values to create their own graphs. The user has control over the title, up to 15 slices, the color of each slice, and can choose a 3-D option.

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  • The applet in this section allows for simple data analysis of univariate data. Users can either generate normal or uniform data for k samples or copy and paste data from another source to a text box. A univariate analysis is performed for all k samples. A two-sample t-test (Pooled and Satterthwaite) is performed for k = 2. An ANOVA test is performed for k > 2. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/Data.html
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  • In this activity, students work in groups to provide practical interpretations of graphs, considering shape, center, and spread. Each group posts their interpretation for one graph and critiques other groups' interpretations on other graphs. Students examine key aspects (shape, spread, location, etc) of histograms and stem plots to develop the ability to interpret graphics. This activity gets the students up and out of their seats and working together. It is a good activity for early in a term. The Gallery Walk idea can be adapted for different sized classes but this activity has been designed for classes up to 65 students.
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  • The applets in this section of Statistical Java address Power. Users can perform one or two tailed tests for proportions or means for one or two samples. Set the parameters and drag the mouse across the graph to see how effect size affects power. An article and an alternative source for this applet can be found at http://www.amstat.org/publications/jse/v11n3/java/power/ This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/Power.html
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  • The applets in this section of Statistical Java allow you to see how levels of confidence are achieved through repeated sampling. The confidence intervals are related to the probability of successes in a Binomial experiment. The main page gives the equation for finding confidence intervals and describes the parameters (p, n, alpha). Each applet allows you to change a different parameter and simulate sampling to demonstrate the long run proportion of intervals that contain the true probability of success. The applets are available from a pull-down menu at the bottom of the page. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/CI.html
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  • This resource defines and explains Chi square. It takes the user through 5 different categories: 1) Testing differences between p and pi 2) More than two categories 3) Chi-square test of independence 4) Reporting results 5) Exercises.

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