Laboratories

  • DataFerrett is a unique data analysis and extraction tool -- with recoding capabilities -- to customize federal, state, and local data to suit your requirements. Using DataFerrett, you can develop an unlimited array of customized spreadsheets that are as versatile and complex as your usage demands. The DataFerrett helps you locate and retrieve the data you need across the Internet to your desktop or system, regardless of where the data resides. You can then develop and customize tables. Selecting your results in your table you can create a chart or graph for a visual presentation into an html page. Save your data in the databasket and save your table for continued reuse. The DataFerrett is a Beta testing version that will incorporate the latest bug fixes, enhancements, and new functionality that will be rolled into the DataFerrett after testing has been completed.

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  • 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.
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  • This issue contains an interview with Sallie Keller-McNulty and an article about which came first -- the chicken or the egg. Other articles include a discussion related to an AP Statistics example of seeing the trees for the forest (this focuses on understanding variability between groups and within groups), a discussion of how high r can go, a simulation piece focused on shrinking students, poisoned children, and bootsraps, and an example of a permutation test of the Challenger O-Ring data.
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  • A searchable database of approximately 600 applets for teaching introductory statistics topics, including graphical displays, descriptive statistics, probability concepts, random variables, sampling and sampling distributions, confidence intervals, hypothesis testing, ANOVA, chi-square tests, correlation and regression, time series and forecasting, decision analysis, and quality control charts. Applets are arranged by topic and intended use. Information on each applet includes source and url as well as a brief description.

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  • May 25, 2010 Activity webinar presented by Ivan Ramler, St. Lawrence University and hosted by Leigh Slauson, Capital University. This webinar discusses an undergraduate Mathematical Statistics course project based on the popular video game Guitar Hero. The project included: 1) developing an estimator to address the research objective "Are notes missed at random?", 2) learning bootstrapping techniques and R programming skills to conduct hypothesis tests and 3) evaluating the quality of the estimator(s) under certain sets of scenarios.

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  • January 26, 2010 webinar presented by Alicia Gram, Smith College, and hosted by Leigh Slauson, Capital University. This webinar describes an activity that uses data collected from an experiment looking at the relationship between two categorical variables: whether a cotton plant was exposed to spider mites; and did the plant contract Wilt disease? The activity uses randomization to explore whether there is a difference between the occurrence of the disease with and without the mites. The webinar includes a discussion of the learning goals of the activity, followed by an implementation of the activity then suggestions for assessment. The implementation first uses a physical simulation, then a simulation using technology. (Extra materials, including Fathom instructions for the simulation, available for download free of charge).

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  • As mentioned on the home page of this resource "This site presents workbook-style, project-based material that emphasizes real world applications and conceptual understanding. This material is designed to give students a sense of the importance and allure of statistics early in their college career. By incorporating many of the successful reforms of the introductory statistics course into a wide range of more advanced topics we hope that students in any discipline can realize the intellectual content and broad applicability of statistics."

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  • March 23, 2010 Activity webinar presented by John Gabrosek & Paul Stephenson, Grand Valley State University and hosted by Leigh Slauson, Capital University. GOLO is a dice-based golf game that simulates playing a round of golf. GOLO can be used to illustrate basic probability concepts, descriptive summaries for data, discrete probability distributions, order statistics, and game theory. Participants had a chance to play the online version of GOLO.
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  • April 27, 2010 Activity webinar presented by Shonda Kuiper, Grinnell College, and hosted by Leigh Slauson, Capital University. Educational games have had varied success in the past. However, what it means to incorporate games into the classroom has changed dramatically in the last 10 years. The goals of these games are to 1) foster a sense of engagement, 2) have a low threat of failure, 3) allow instructors to create simplified models of the world around us, and 4) motivate students to learn. This webinar uses the same reaction time game to demonstrate a simple 1- 2 day activity that is appropriate for introductory courses as well as an advanced project that encourages students to experience data analysis as it is actually practiced in multiple disciplines. In the introductory activity students are asked to spend 15 minutes playing an on-line game. Data collected from the game is used to demonstrate the importance of proper data collection and appropriate statistical analysis. The advanced project asks students to read primary literature, plan and carry out game based experiments, and present their results.
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  • An important idea in statistics is that the amount of data matters. We often teach this with formulas --- the standard error of the mean, the t-statistic, etc. --- in which the sample size appears in a denominator as √n. This is fine, so far as it goes, but it often fails to connect with a student's intuition. In this presentation, I'll describe a kinesthetic learning activity --- literally a random walk --- that helps drive home to students why more data is better and why the square-root arises naturally and can be understood by simple geometry. Students remember this activity and its lesson long after they have forgotten the formulas from their statistics class.

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