Resource Library

This resource assists the user in reading, constructing, and understanding confidence intervals.

Students can sample numerous bags of M&Ms. A plot of the relative frequency of each color is continually updated above the sampling frame. Each sample bag of M&Ms contains 56 candies.

This applet simulates families of three children. The probability of having a boy on any attempt can be changed in the parameter statement. The percantage of times "x" number of girls occurs is updated in the bar chart. There is a 2nd applet on the page that is the same as above, but the families stop having children after the first boy or after they have had 3 girls.

Part of an online statistics textbook. Topics include: (1) Law of Large Numbers for Discrete Random Variables, (2) Chebyshev Inequality, (3) Law of Averages, (4) Law of Large Numbers for Continuous Random Variables, (5) Monte Carlo Method. There are several examples and exercises that accompany the material.

This lesson on the Poisson distribution explains the theory, history, and applications of the distribution and gives examples and a multiple choice test.

These lecture notes are composed of nearly 180 PowerPoint slides that have been coverted to a pdf file (6 per page) on Biomedical Imaging. The following topics are outlined: Vocabulary, Displaying Data, Central Tendency and Variability, Normal Z-scores, Standardized Distribution, Probability, Samples & Sampling Error, Type I and Type II Errors, Power of a Test, Hypothesis Testing, One Sample Tests, Two Independent Sample Tests, Two Dependent Sample Tests & Estimation, Correlation and Regression Techniques, Non-Parametric Statistical Tests, Applications of Central Limit Theorem, Law of Large Numbers, Design of Studies and Experiments, Fisher's F-Test, Analysis Of Variance(ANOVA), Principle Component Analysis (PCA), Chi-Square Goodness-of-fit test, Multiple Linear Regression, General Linear Model, Bootstrapping and Resampling.

For n = 50 to 400, in steps of size 5, this program computes and displays (1) the exact probability P(|A_n - p| >= epsilon), where A_n is the average outcome of n Bernoulli trials with probability p of success, and (2) the Chebyshev estimate p(1-p)/(n(epsilon^2)) for this probability. You can specify p and epsilon.

This PowerPoint presentation teaches sampling distributions related to proportions and means using multiple examples, charts, and graphs.

The t-distribution activity is a student-based in-class activity to illustrate the conceptual reason for the t-distribution. Students use TI-83/84 calculators to conduct a simulation of random samples. The students calculate standard scores with both the population standard deviation and the sample standard deviation. The resulting values are pooled over the entire class to give the simulation a reasonable number of iterations.

This tutorial introduces 9 sources of threat to internal vailidity and asks the user to classify hypothetical experiments as either internally valid or invalid and identify the source of threat if invalid.