Resource Library

This resource defines and explains binomial probability, including examples and exercises for the learner.

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

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

This is a collection of applets regarding various topics in statistics. Topics include Central Limit Theorem, Probability Distributions, Hypothesis Testing, Power, Confidence Intervals, Correlation, Control Charts, Experimental Design, Data Analysis, and Regression. Each topic has a description page and links to one or more applets. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/

The applets in this section of Statistical Java allow you to see how the Central Limit Theorem works. The main page gives the characteristics of five non-normal distributions (Bernoulli, Poisson, Exponential, U-shaped, and Uniform). Users then select one of the distributions and change the sample size to see how the distribution of the sample mean approaches normality. Users can also change the number of samples. To select between the different applets you can click on Statistical Theory, the Central Limit Theorem and then the Main Page. At the bottom of this page you can make your applet selection. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/

The site provides an introduction to understand the basics of and working with Excel. Redoing the illustrated numerical examples in this site will help in improving your familiarity, and as a result, increase the effectiveness and efficiency of your process in statistics.

A pinch of probability is worth a pound of perhaps. A quote by American humorist and cartoonist James Thurber (1894 - 1961). The quote appeared in "Such a Phrase as Drifts Through Dreams," a short story in Thurber's last book, "Lanterns and Lances", Harper Publishing, 1961. The quote also appears in "Statistically Speaking: A dictionary of quotations" compiled by Carl Gaither and Alma Cavazos-Gaither.

This applet allows you to explore the validity of confidence intervals on a proportion with various values for sample size (N) and population proportion (Pi). After you specify N, Pi, the level of confidence, and the number of simulations you wish to perform, the applet samples data according to your specification and computes a confidence interval for each simulation. The proportion of simulations for which the confidence interval contains Pi is recorded. If the method for constructing confidence intervals is valid, then about 95% of the 95% confidence intervals should contain Pi.

This simulation applet shows groups of confidence intervals for a given alpha based on a standard normal distribution. It shows how changes in alpha affect the proportion of confidence intervals that contain the mean. An article and an alternative source for this applet can be found at http://www.amstat.org/publications/jse/v6n3/applets/confidenceinterval.html.

This section of the Engineering Statistics Handbook gives the normal probability density function as well as the standard normal distribution equations. Example graphs of the distributions are shown and a justification of the Central Limit Theorem is included.