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 case study assesses the question, "Do physicians discriminate against overweight patients?" This study indicates that, at least in one respect, they do. Concepts: t-test, means, boxplots

This free online video program "marks a transition in the series: from a focus on inference about the mean of a population to exploring inferences about a different kind of parameter, the proportion or percent of a population that has a certain characteristic. Students will observe the use of confidence intervals and tests for comparing proportions applied in government estimates of unemployment rates."

This interactive tutorial on Basic Probability helps students understand the basic concepts of probability, define independent and compound events, use the basic properties of probability, understand the concept of conditional probability, and solve exercise problems using basic probability.

This tutorial on Random Variables helps students understand the definition of random variables, recognize and use discrete random variables, recognize and use continuous random variables, and solve exercise problems using random variables.

This interactive tutorial on Expectations helps students understand the concept of expectations, recognize and use variance and standard deviation, understand the method of moments, recognize and use co-variance, and solve exercise problems using expectations.

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.