Teaching/Illustration Templates

This template demonstrates the random nature of confidence intervals. It generates random samples from a variable and plots confidence intervals for the variable mean generated by those samples.

This template generates pictures of certain kinds of fractal sets. Technically, it runs iterated affine function systems. Pictures include the Sierpinski gasket, ferns, and trees. Includes a file with some sample parameter settings. Note: Users with monitors smaller than 800x600 may have difficulties using this template.

A fun and interesting build of the famous Mandelbrot Set fractal.

This template demonstrates regression confidence intervals. One somewhat difficult topic in teaching regression is explaining to students why confidence intervals for regression lines are hyperbolic in shape. This template allows one to visualize the process.

The user has control over the sample size, the number of samples, the amount of error variance and heteroskedasticity (non-constant variance). Adjusting the error heteroskedasticity reveals hyperbolic shapes with narrow cones on one end and fat cones on the other end. Increase the sample size to get tighter intervals.

This template demonstrates the empirical sampling distribution of a sample mean. This can be used to demonstrate the Central Limit Theorem without the restriction of sampling from a uniform population. This template also demonstrates the empirical sampling distribution of the difference between means. It can be used to test the hypothesis of equality of means through resampling rather than parametric methods.

This template draws prediction and confidence interval bands for a simple regression of Y vs. X on a scatterplot of the data. It also calculates the exact endpoints of these intervals for a user-defined X-value. The user has control over the confidence level, the X-value for the calculated interval, and the color of the lines on the interval plot.

This program illustrates the Central Limit Theorem. A random uniform variable with a given number of cases is generated. Its mean is computed and appended to the end of a variable which is plotted in a histogram and probability plot.

This template draws a scatterplot and the user has control over the best-fitting line. If the user changes the intercept and slope, then the line automatically moves and summary statistics are automatically computed and updated. This gives insight into minimizing SSE and R2. The user also controls the error structure to see its effect on the regression and the scatterplot.

Finally, the user can hit a button to automatically minimize the best-fitting line using least squares (sum of squares) or sum of absolute error.

Specialized Plots
Templates primarily concerned with displaying data.

Specialized Statistics
Templates that perform specialized computations.

Advanced Analyses
Templates that perform whole analyses, usually involving both statistics and plots.

Teaching/Illustration
Templates and simulations whose primary purpose is educational in nature.

Quick Pick
A list of template names only, designed for quick access to specific templates.

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