Webinars

This webinar will discuss what it means to understand the concept of standard deviation and share a sequence of activities that can be used to help students develop variability. Particular attention will be paid to one activity that challenges students to work together to consider center and variability together, developing a better understanding of the standard deviation. The activities discussed are accompanied by detailed Lesson Plans, developed through our NSF-funded AIMS project.

This webinar will describe 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 will include 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.

A real-time online hands-on activity database is introduced for teaching introductory statistics. The activity, "How well can hand size predict height?", will be used to demonstrate how to use real-time activity to engage students to learn bivariate relationship. Various other activities can be found at stat.cst.cmich.edu/statact. The real-time database approach speeds up the process of data gathering and shifts the focus to engage students in the process of data production and statistical investigation.

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.

This webinar is based on the activity I found at www.lhs.logan.k12.ut.us/~jsmart/tank.htm and other on-line resources (see references). During World War II, the British and U.S. statisticians used estimation methods to deduce the productivity of Germany's armament factories using serial numbers found on captured equipment, such as tanks. The tanks were numbered in a manner similar to 1, 2, 3, ..., N, and the goal of the allies was to estimate the population maximum N from their collected sample of serial numbers. The purpose of this activity is to introduce students to the concept of an unbiased estimator of a population parameter. Students will develop several estimators for the parameter N and compare them by running simulations in Minitab.

In a classroom setting, students can engage in hands-on activities in order to better understand certain concepts and ideas. Replicating hands-on activities in an online environment, however, can be a challenge for instructors. The purpose of this webinar is to present an applet that was created to replicate a "Post-it Note" activity we commonly use in classroom sections of an undergraduate introductory statistics course. The Post-it Note activity is meant to help students develop a more conceptual understanding of the mean and the median by moving a set of Post-it Notes along a number line. During the webinar, participants will have an opportunity to see and experience just how online students are able to interact with what we have named the "Sticky Centers" applet, and we will present the kinds of materials and assignments we have created to use in conjunction with this applet. The webinar will end with a preview a newer applet we are working on in order to replicate the famous "Gummy Bears in Space" activity (presented in Schaeffer, Gnanadesikan, Watkins & Witmer, 1996).

Based on an activity by John Spurrier, we use 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. We also compare our different Bayesian estimators and different frequentist estimators using bias, variability, and mean squared error. We can see the effect that sample size and dispersion of the prior distribution have on the estimator.

This webinar will describe an activity that uses the playlist from an iPod music player to teach the concept of random selection, the various sampling techniques, and the use of simulation to estimate probability. The webinar will include a discussion of the background of this activity, the learning goals of the activity, how this activity can be adapted to different levels of technology, suggestions for assessment, and other supplemental reference materials.

This webinar will describe a computer lab activity using the Flash-based applet at www.causeweb.org/mouse_experiment to teach key principles regarding the value of random assignment: how it helps to eliminate bias when compared with a haphazard assignment process, how it leads to a consistent pattern of results when repeated, and how it makes the question of statistical significance interesting since differences between groups are either from treatment or by the luck of the draw. In this webinar, the activity will be demonstrated along with a discussion of goals, context, background materials, class handouts, and assessments.

Getting and retaining the attention of students in an introductory statistics course can be a challenge, and poor motivation or outright fear of mathematical concepts can hinder learning. By using an example as familiar and comforting as chocolate chip cookies, the instructor can make a variety of statistical concepts come to life for the students, greatly enhancing learning. Topics from variability and exploratory data analysis to hypothesis testing and Bayesian statistics can be illuminated with cookies.