# Other

• ### Independent Samples t-Test: Chips AhoyŒ¬ vs. Supermarket Brand

In this hands-on activity, students count the number of chips in cookies in order to carry out an independent samples t-test to see if Chips AhoyŒ¬ cookies have a higher, lower, or different mean number of chips per cookie than a supermarket brand. First there is a class discussion that can include concepts about random samples, independence of samples, recently covered tests, comparing two parameters with null and alternative hypotheses, what it means to be a chip in a cookie, how to break up the cookies to count chips, and of course a class consensus on the hypotheses to be tested. Second the students count the number of chips in a one cookie from each brand, and report their observations to the instructor. Third, the instructor develops the independent sample t-test statistic. Fourth, the students carry out (individually or as a class) the hypothesis test, checking the assumptions on sample-size/population-shape.
• ### **One-way ANOVA Demonstration

Tutorial on the ANOVA test in statistics and probability, with a description, formulas, example, and a calculator applet. This is part of the larger site Virtual Statistician at http://web.mst.edu/~psyworld/virtualstat.htm
• ### Song: Stat 51: I Will Survive

Song about the use of the 5-number summary to describe skewed data as an alternative to the mean and standard deviation. May be sung to the tune of the 1979 song "I Will Survive" by Gloria Gaynor. Lyrics written by Sheila O'Leary Weaver. The song took first place in the song category in the 2007 A-Mu-Sing competition. Musical accompaniment realization are by Joshua Lintz and vocals are by Mariana Sandoval from University of Texas at El Paso.

• ### Song: MLE

Song about the properties of Maximum Likelihood Estimation including efficiency, invariance, and asymptotic normality. May sing to the tune of "Let it Be" By Paul McCartney. Recorded June 26, 2009 at the OSU Whisper Room: Larry Lesser, vocals/guitar; Justin Slauson, engineer.

• ### Instructors Notes for the t-distribution activity

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.
• ### Categorical Data Activity

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
• ### Introduction to Experiments Activity

This activity will allow students to learn the difference between observational studies and experiments, with emphasis on the importance of cause-and-effect relationships. The activity will also familiarize students with key terms such as factors, treatments, retrospective and prospective studies, etc.
• ### **Inference for Means Activity

This activity enables students to learn about confidence intervals and hypothesis tests for a population mean. It focuses on the t-distribution, the assumptions for using it, and graphical displays. The activity also focuses on how to interpretations a confidence interval, a p-value, and a hypothesis test.

• ### Inference for Proportions Activity

This activity provides practice for constructing confidence intervals and performing hypothesis tests. In addition, it stresses interpretation of confidence intervals and comparison and application of results in context.
• ### Quantitative Data Activity

This activity explains the important features of a distribution: shape, center, spread, and unusual features. It also covers how to determine the difference between mean and median, and their respective measures of spread, as well as when to apply them to a particular distribution. Graphical displays such as: histograms and boxplots are also introduced in this activity. The corresponding data set for this activity is found at the following web address: http://www.causeweb.org/repository/ACT/food.txt