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 tutorial on Distributions helps students understand the basic concept of probability distributions, recognize and use Binomial, Normal, Poisson, and Uniform Distributions, and solve exercise problems using probability distributions.

This self-test provides a review/assessment of the Probability section of this module. At the bottom, there is a grading button to rate the users' understanding of the material.

This tutorial on Multiple Regression helps students understand the definition, use the standard error of estimate, use rank correlation, and solve exercise problems using multiple regression.

This interactive tutorial on Linear Regression helps the user understand the definition of linear regression, understand the meaning of correlation, use scatter plots, recognize and calculate errors in linear regression, use simple linear regression analysis, use residual analysis of the regression equation, understand the significance of the correlation coefficient and the regression coefficient in linear regression, and solve exercise problems using linear regression.

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. "