Resource Library

The Comprehensive Epidemiologic Data Resource is a collection of data sets. It includes definitions of each variable in the data set. It requires a login to retrieve the data sets. Registering involves giving your name and address and the name of the study and a detailed description of the intended use of the data.

This complete lesson plan, which includes assessments, is based upon a data set partially discussed in the article "Female Hurricanes are Deadlier than Male Hurricanes." The data set contains archival data on actual fatalities caused by hurricanes in the United States between 1950 and 2012. Students analyze and explore this hurricane data in order to formulate a question, design and implement a plan to collect data, analyze the data by measures and graphs, and interpret the results in the context of the original question.

The STatistics Education Web, also called STEW, is an online collection of peer-reviewed statistics lesson plans for K-12 teachers. The web site is maintained by the ASA and accessible to K-12 teachers throughout the world. Lessons cover a wide range of probability and statistics topics.

The textbook website for "Statistics: The Art & Science of Learning from Data," by Agresti, Franklin and Klingenberg, has a collection of Shiney Apps for visualizing statistical concepts. There are usable on computers, tablets and smart phones. Apps include Exploratory Analysis, Random Numbers, Association and Linear Regression, Distributions, Probabilities, Sampling distributions, Central Limit Theorem,, Inferential Methods (one and two samples), ANOVA, and Bootstrap Confidence Intervals & Permutation Tests.

This webpage provides an active learning lesson for linear regression. Resources include an in-class student activity sheet for two different levels of classes (Algebra I and Junior), a PowerPoint showing faces of famous people, and sheet with updated (to the end of current year) actual ages of the celebrities.

September 28, 2010 Activity webinar presented by Carolyn Cuff, Westminster College and hosted by Leigh Slauson, Capital University. Extra materials available for download free of charge. Students must confront their misconceptions before we can teach them new concepts. Naively, a census is an accurate method to quantify a population parameter. A very brief, memorable and easy to implement activity demonstrates that a census is at best difficult even for a small and easily enumerated population.

August 24, 2010 Activity Webinar presented by Jackie Miller, The Ohio State University and hosted by Leigh Slauson, Capital University. Extra materials available for download free of charge. When Dr. Miller took a graduate course in College Teaching, she learned the jigsaw method. The jigsaw is a cooperative learning technique where students work together in a "home" group on a specific task and then are placed into "jigsaw" groups made up of one member from each home group. For example, if there are 25 students in the class, 5 students would be assigned to each of the A, B, C, D, E home groups, and each jigsaw group would each one member from A, B, C, D, and E. While in the jigsaw groups, the students teach each other what they learned in their home groups. Dr. Miller recalls bringing the idea back with her to one of the OSU elementary statistics courses where it has been used successfully since 1996. Recently a graduate teaching assistant (GTA) suggested to other GTAs that this might be good in another introductory statistics course, and the activity has been adopted successfully . As structured, the jigsaw can be used in an exam review in statistics by assigning students to, say, 5 exercises that they need to master before they go to their jigsaw groups to teach others about their exercise. During this webinar, the webinar presents how the jigsaw is done and address questions like: How do you budget your time for this class activity? How do you know that students are teaching the correct answer? How do you know that students are not just furiously writing down answers instead of listening to understand the concept? Can this work for you? By the end of the webinar, hopefully you will be as intrigued as Dr. Miller was to learn about the jigsaw method and will want to try it in your classroom.

July 27, 2010 Activity Webinar presented by Herle McGowan, North Carolina State University and hosted by Leigh Slauson, Capital University. Extra materials available for download free of charge. In this webinar, the webinar discusses the end-of-semester project that is used in North Carolina State's introductory statistics course. This project supports statistical thinking by allowing students to apply knowledge accumulated throughout the semester. Students are presented with a research question and must design and carry out an experiment, analyze the resulting data and form a conclusion over the course of several class periods.

July 13, 2010 T&L webinar presented by Webster West (Texas A&M University) and hosted by Jackie Miller(The Ohio State University). In introductory statistics courses, web-based applets are often used to visually conduct large simulation studies illustrating statistical concepts. However, it is difficult to determine what (if anything) students learn from repeatedly pressing a button when using applets. More advanced options such as writing/running computer code are typically considered to be much too advanced for most introductory courses. The web-based software package, StatCrunch, now offers simulation capabilities that strike a middle ground between these two extremes. The instructor/student needs only to perform a small number of steps using the menu driven interface with each step being key to understanding the underlying data structure. This talk will cover the steps required to study concepts such as the central limit theorem, confidence intervals, hypothesis testing and regression using StatCrunch.

This issue contains an article that provides an example of a paired samples test related to flying and gliding. It also includes an article about understanding confounding from lurking variables using graphs. Other articles include: a short description about what the t-tests actually tests, an interview with David Moore about why 30 is the "magic" number, a discussion about whether or not outliers should be deleted from a data set, a discussion of observational studies, and a simulation piece about random numbers from non-random arithmetic.