Resource Library

This activity will allow students to familiarize themselves with technology and its use in calculating marginal, conditional, and joint distributions, as well as making conclusions from these tabular and graphical displays. The corresponding data set 'Pizza Data' is located at the following web address: http://www.causeweb.org/repository/ACT/PIZZA.TXT

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 document provides the instructor with learning objectives, context, mechanics, follow-up, and evidence from use associated with the in-class activity.

This activity represents a very general demonstration of the effects of the Central Limit Theorem (CLT). The activity is based on the SOCR Sampling Distribution CLT Experiment. This experiment builds upon a RVLS CLT applet (http://www.ruf.rice.edu/~lane/stat_sim/sampling_dist/) by extending the applet functionality and providing the capability of sampling from any SOCR Distribution. Goals of this activity: provide intuitive notion of sampling from any process with a well-defined distribution; motivate and facilitate learning of the central limit theorem; empirically validate that sample-averages of random observations (most processes) follow approximately normal distribution; empirically demonstrate that the sample-average is special and other sample statistics (e.g., median, variance, range, etc.) generally do not have distributions that are normal; illustrate that the expectation of the sample-average equals the population mean (and the sample-average is typically a good measure of centrality for a population/process); show that the variation of the sample average rapidly decreases as the sample size increases.

Song is about formal constructions of probability theory. May be sung to the tune of "Strawberry Fields" by John Lennon and Paul McCartney. Musical accompaniment realization and vocals are by Joshua Lintz from University of Texas at El Paso.

A cartoon to teach about proper reporting of statistical results such as conclusions from a significance test. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.

A cartoon to teach about the measurement issues of bias, reliability, and validity. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.

In God we trust, all others bring data. An unsourced quote often attributed to American statistician and quality control pioneer William Edwards Deming (1900-1993). The quote has also been a motto of NASA for several decades.

The Theory of probabilities is simply the science of logic quantitatively treated. A quote by American logician Charles Saunders Peirce (1839 - 1914). The quote may be found in "Writings of Charles Saunders Peirce, volume 3, 1872-1878" p. 278 as cited in "Statistically Speaking: A dictionary of quotations" compiled by Carl Gaither and Alma Cavazos-Gaither.

This is a large collection of statistics related jokes and humor compiled by Gary C. Ramseyer. The collection is indexed by statistical topic for ease of use.

The generation of random numbers is too important to be left to chance. The title of a 1969 article by American Mathematician and civil rights activist Robert R. Coveyou (1915 - 1996). ("Appl. Math.," 3 p. 70-111)