Resource Library

The applet in this section allows you to see how the T distribution is related to the Standard Normal distribution by calculating probabilities. The T distribution is primarily used to make inferences on a Normal mean when the variance is unknown. If the variance is known inference on the mean can be done using the Standard Normal. The user has a choice of three different probability expressions, then can change the degrees of freedom and the limits of probability. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/TNormal.html

This is an interactive tutorial on Data Analysis topics including representations of data, understand the definition of mean, understand the definition of variance, recognize a few other useful concepts, recognize various sampling techniques, and solve exercise problems using data analysis.

This tutorial includes using, finding, weighting, and solving problems with Moving Averages.

This demonstration allows you to view the binomial distribution and the normal approximation to it as a function of the probability of a success on a given trial and the number of trials. It can be used to compute binomial probabilities and normal approximations of those probabilities.

This interactive tutorial on Exponential Smoothing helps learners understand the use of exponential smoothing, define exponential smoothing, cite the merits and demerits of exponential smoothing, and solve exercise problems using exponential smoothing.

As described on the page itself: "The simulation shows a scatterplot of data from a bivariate distribution in which the relationship between the two variables is linear. You can change the "input" values of slope, standard error of the estimate, or standard deviation of X for this data sample, and see the effects of your change. "

This interactive module helps students to understand the definition of and uses for clustering algorithms. Students will learn to categorize the types of clustering algorithms, to use the minimal spanning tree and the k-means clustering algorithm, and to solve exercise problems using clustering algorithms.

In this demonstration a scatterplot is displayed and you draw in a regression line by hand. You can then compare your line to the best least squares fit. You can also try to guess the value of Pearson's correlation coefficient.

This module is a short quiz which gives a review/assessment of the main concepts for this refresher course. At the bottom, there is a grading button to rate the understanding of the material.

This applet shows how the correlation between two variables is affected by the range of the variable plotted on the X-axis.