Resource Library

In this game activity, students match correlation values with plots generated by the applet. Competition in this game setting encourages students to become more involved in the classroom and attainment of learning objectives. This game is best if used in a lab setting, although it may be modified to fit other classroom situations.

This activity begins with an instructor demonstration followed by a student out-of-class assignment. Students will observe their instructor create a scatterplot and observe how the correlation coefficient changes when outlier points are added. Students are then given a follow up assignment, which guides them through the applet. In addition, the assignment provides insight about outliers and their effect on correlation. This activity will show exactly how outliers numerically change the correlation coefficient value and to what degree.

This visualization activity combines student data collection with the use of an applet to enhance the understanding of the distributions of slope and intercept in simple linear regression models. The applet simulates a linear regression plot and the corresponding intercept and slope histograms. The program allows the user to change settings such as slope, standard deviation, sample size, and more. Students will then see theoretical distributions of the slope and intercept and how they compare to the histograms generated by the simulated linear regression lines.

This in-class demonstration combines real world data collection with the use of the applet to enhance the understanding of sampling distribution. Students will work in groups to determine the average date of their 30 coins. In turn, they will report their mean to the instructor, who will record these. The instructor can then create a histogram based on their sample means and explain that they have created a sampling distribution. Afterwards, the applet can be used to demonstrate properties of the sampling distribution. The idea here is that students will remember what they physically did to create the histogram and, therefore, have a better understanding of sampling distributions.

This site funded by the Kaiser Family Foundation provides information on health care and demographics for the 50 U.S. states. Users can use interactive maps or search by particular characteristics for each state. Tables can be created and copied and there is also direct data download (in Excel format) from this site. The site includes data on median income, gender, ethnicity, medical and drug spending, HIV/AIDS rates, and over 500 other variables at the state level

Meditation on Statistical Method is a poem by American poet and Brandeis University professor James Vincent Cunningham (1911 - 1985). The poem was originally published in "The Exclusions of a Rhyme: Poems and Epigrams" (1960; Swallow Press) and may also be found in "The collected poems and epigrams of J.V. Cunningham" (1971; Swallow Press).

This limerick was written by Columbia University professor of biostatistics, Joseph L. Fleiss (1938 -2003). It was published along with three other limericks by Dr. Fleiss in a letter to the editor of "The American Statistician" (volume 2; 1967, page 49). It was written while he worked as a biostatistician at the Department of Mental Hygiene of the State of New York just prior to receiving his Ph.D. and joining the faculty at Columbia.

A series of 19 songs used to teach Structural Equations Modeling (SEM) by Alan Reifman of Texas Tech University. A video of an in-class performance of the musical on April 27, 2007, is also available at the website. The Musical took second place in the 2007 A-Mu-sing competition.

This pdf text file gives a short introduction to the methods of Bayesian inference. It gives a simple example that deals with jumping a paper frog. The topics listed in this document include: An example, comparison of frequentist and Bayesian methods, credible vs. confidence intervals, choice of prior and its effect on the posterior distribution.

This activity is an example of Cooperative Learning in Statistics. It uses student's own data to introduce bivariate relationship using hand size to predict height. Students enter their data through a real-time online database. Data from different classes are stored and accumulated in the database. This real-time database approach speeds up the data gathering process and shifts the data entry and cleansing from instructor to engaging students in the process of data production. Key words: Regression, correlation data collection, body measurements