Resource Library

July 28, 2009 Activity webinar presented by Jo Hardin, Pomona College, and hosted by Leigh Slauson, Otterbein College. Based on an activity by John Spurrier, this webinar uses a baseball example to introduce students to Bayesian estimation. Students use prior information to determine prior distributions which lead to different estimators of the probability of a hit in baseball. The webinar also compares different Bayesian estimators and different frequentist estimators using bias, variability, and mean squared error. The effect that sample size and dispersion of the prior distribution have on the estimator is then illustrated by the activity.

Webinar recorded May 9, 2006 presented by Carl Lee of Central Michigan University and hosted by Jackie Miller of The Ohio State University. Do you use hands-on activities in your class? Would you be interested in using data collected by students from different classes at different institutions? Would you be interested in sharing your students' data with others? Does it take more time than you would like to spend in your class for hands-on activities? Do you have to enter the hands-on activity data yourself after the class period? If your answer to any of the above questions is "YES", then, this Real-Time Online Database approach should be beneficial to your class. In this presentation, Dr. Lee (1) introduces the real-time online database (stat.cst.cmich.edu/statact) funded by a NSF/CCL grant, (2) demonstrates how to use the real-time database to teach introductory statistics using two of the real-time activities and (3) shares with you some of the assessment activities including activity work sheets and projects.

The AIMS project developed lesson plans and activities based on innovative materials that have been produced in the past few years for introductory statistics courses. These lesson plans and student activity guides were developed to help transform an introductory statistics course into one that is aligned with the Guidelines for Assessment and Instruction in Statistics Education (GAISE) for teaching introductory statistics courses. The lessons build on implications from educational research and also involve students in small and large group discussion, computer explorations, and hands-on activities.

A cartoon that might be used in introducing scatterplots and correlation. 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.

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

This activity makes use of a campus-based resource to develop a "capstone" project for a survey sampling course. Students work in small groups and use a complex sampling design to estimate the number of new books in the university library given a budget for data collection. They will conduct a pilot study using some of their budget, receive feedback from the instructor, then complete data collection and write a final report.

In this activity, students explore calculations with simple rates and proportions, and basic time series data, in the context of news coverage of an important statistical study. From 1973 to 1995, a total of 4578 US death penalty cases went through the full course of appeals, with the result that 68% of the sentences were overturned! Reports of the study in various newspapers and magazines fueled public debate about capital punishment.

In this activity, students learn the true nature of the chi-square and F distributions in lecture notes (PowerPoint file) and an Excel simulation. This leads to a discussion of the properties of the two distributions. Once the sum of squares aspect is understood, it is only a short logical step to explain why a sample variance has a chi-square distribution and a ratio of two variances has an F-distribution. In a subsequent activity, instances of when the chi-square and F-distributions are related to the normal or t-distributions (e.g. Chi-square = z2, F = t2) will be illustrated. Finally, the activity will conclude with a brief overview of important applications of chi-square and F distributions, such as goodness-of-fit tests and analysis of variance.

This group activity illustrates the concepts of size and power of a test through simulation. Students simulate binomial data by repeatedly rolling a ten-sided die, and they use their simulated data to estimate the size of a binomial test. They carry out further simulations to estimate the power of the test. After pooling their data with that of other groups, they construct a power curve. A theoretical power curve is also constructed, and the students discuss why there are differences between the expected and estimated curves. Key words: Power, size, hypothesis testing, binomial distribution

This activity allows students to explore the relationship between sample size and the variability of the sampling distribution of the mean. Students use a Java applet to specify the shape of the "parent" distribution and two sample sizes. The simulation then samples from the parent distribution to approximate the sampling distributions for the two sample sizes. Students can see both sampling distributions at the same time making them easy to compare. The activity also allows students to determine the probability of extreme sample means for the different sample sizes so that they can discover that small sample sizes are much more likely than large samples to produce extreme values. Keywords: sampling distribution, sample size, simulation