Webinars

What's the best way to introduce students of mathematics to statistics? Tradition offers two main choices: a variant of the standard "Stat 101" course, or some version of the two-semester sequence in probability and mathematical statistics. I hope to convince participants to think seriously about a third option: the theory and applications of linear models as a first statistics course for sophomore math majors. Rather than subject you to a half-hour polemic, however, I plan to talk concretely about multiple regression models and methodological challenges that arise in connection with AAUP data relating faculty salaries to the percentage of women faculty, and to present also a short geometric proof of the Gauss-Markov Theorem.

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. Exercise Documentation

Permutation tests and randomization tests were introduced almost a century ago, well before inexpensive, high-speed computing made them feasible to use. Fisher and Pitman showed the two-sample t-test could approximate the permutation test in a two independent groups experiment. Today many statistics educators are returning to the permutation test as a more intuitive way to teach hypothesis testing. In this presentation, I will show an interesting teaching example about primate behavior that illustrates how simple permutation tests are to use, even with a messier data set that admits of no obvious and easy-to-compute approximation.

When I took a graduate course in College Teaching, I 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. I recall bringing the idea back with me to our elementary statistics course where it has been used successfully since 1996. Recently a graduate teaching assistant (GTA) suggested to other GTAs that this might be good in our 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, I will present 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 I was to learn about the jigsaw method and will want to try it in your classroom.

Our research shows that half of the students entering a statistics course use the word random colloquially to mean, "haphazard" or "out of the ordinary." Another large subset of students define random as, "selecting without prior knowledge or criteria." At the end of the semester, only 8% of students we studied gave a correct statistical definition for the word random and most students still define random as, "selecting without order or reason." In this session we will present a classroom approach to help students better understand what statisticians mean by random or randomness as well as preliminary results of the affect of this approach.

In this webinar, I will discuss 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.

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 webinar will describe an in-class activity, motivated by Case Study 1.1.1 in The Statistical Sleuth, in which students compose haiku poems about statistics. Their poems are used to introduce two-sample t-tests and randomization tests. In addition, the in-class experiment leads to good discussion about experimental design issues, where students compare our design to the actual experiment described in Amabile et al.(1985; "Motivation and Creativity: Effects of Motivational Orientation on Creative Writers", Journal of Personality and Social Psychology 48(2): 393-399). I use this activity on the first day of our second course in applied statistics (Statistical Modeling), but it could easily be used in an introductory course as well. Examples of haiku poems which have resulted from this activity can be found at www.causeweb.org/cwis/SPT--FullRecord.php?ResourceId=1883.

This webinar will introduce several sources of data and tools that could be useful in both general and social science-specific statistics instruction. The Social Science Data Analysis Network (SSDAN) and the Inter-university Consortium for Political and Social Research (ICPSR), both a part of the University of Michigan's Institute for Social Research, are collaborating on two NSF-funded projects to support quantitative literacy in the social sciences. Resources from each organization and TeachingWithData.org, a result of the partnership, will be highlighted. Materials range from small extracts of data from the Census and American Community Surveys used with specific teaching modules to full datasets with accompanying online analysis tools.

This webinar will discuss an undergraduate Mathematical Statistics course project based on the popular video game Guitar Hero. The project included: Developing an estimator to address the research objective "Are notes missed at random?" Learning bootstrapping techniques and R programming skills to conduct hypothesis tests and Evaluating the quality of the estimator(s) under certain sets of scenarios.