Resource Library

In the "Mathematics & Statistics" section on the "Faculty Showcase" tab, one can find a free, online statistics textbook (and link to other text resources) along with multiple professors' accounts of how they use this text in their respective classrooms.  On each professor's page is a description of the course taught, what caused each instructor to switch texts, how the text/course material has been received by students, and a sample assignment/syllabus from the course.  This is a wealth of information for those looking to switch books or gain insight into other professors' classes.

TeachingWithData.org is portal of teaching and learning resources for infusing quantitative literacy into the social science curriculum. A Pathway of the National Science Digital Library, TwD aims to support the social science instructor at secondary and post-secondary schools by presenting user-friendly, data-driven student exercises, pedagogical literature, and much more! Resources are available on a wide range of topics and disciplines.

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.

Many of us, while teaching an introductory statistics course, have mentioned some of the history behind the methodology, perhaps just in passing. We might remark that an English chap by the name of R. A. Fisher is responsible for a great deal of the course content. We could further point out that the statistical techniques used in research today were developed within the last century, for the most part. At most, we might reveal the identity of the mysterious "Student" when introducing the t-test to our class. I propose that we do more of this. This webinar will highlight some opportunities to give brief history lessons while teaching an introductory statistics course.

August 14, 2007 Teaching & Learning webinar presented by Oded Meyer, Carnegie Mellon University, and hosted by Jackie Miller, The Ohio State University. Carnegie Mellon University was funded to develop a "stand-alone" web-based introductory statistics course, and for several semesters they studied different ways in which the course could be used to support instruction. In this presentation, Dr. Meyer discusses some of the challenges in developing such a learning environment and ways in which the course tries to address them, as well as describing the design and results of accompanying studies.

December 11, 2007 Teaching and Learning webinar presented by Mark L. Berenson, Montclair State University, and hosted by Jackie Miller, he Ohio State University. As we consider how we might improve our introductory statistics courses, we are constrained by a variety of environmental/logistical and pedagogical issues that must be addressed if we want our students to complete the course saying it was useful, it was relevant and practical, and that it increased their communicational, computational, technological and analytical skills. If not properly considered, such issues may result in the course being considered unsatisfying, incomprehensible, and/or unnecessarily obtuse. This Webinar focuses on key course content concerns that must be addressed and engages participants in discussing resolutions. Participants also had the opportunity to describe and discuss other content barriers to effective statistical pedagogy.

April 8, 2008 Teaching and Learning webinar presented by Beth Chance and Allan Rossman, Cal Poly - San Luis Obispo and hosted by Jackie Miller, The Ohio State University. Math majors, and other mathematically inclined students, have typically been introduced to statistics through courses in probability and mathematical statistics. We worry that such a course sequence presents mathematical aspects of statistics without emphasizing applications and the larger reasoning process of statistical investigations. This webinar describes and discusses a data-centered course that we have developed for mathematically inclined undergraduates.

May 12, 2009 Teaching and Learning hour-long webinar panel discussion presented by Laura Kubatko, The Ohio State University; Danny Kaplan, Macalester College; and Jeff Knisley, East Tennessee State University, and hosted by Jackie Miller, The Ohio State University. National reports such as Bio2010 have called for drastic improvements in the quantitative education that biology students receive. The three panelists are involved in three differently structured integrative programs aimed to give biology students the statistics that are useful in learning and doing biology. The three programs have some surprising things in common for teaching introductory statistics. All three involve connecting calculus and statistics. All three reach beyond the mathematical topics usually encountered in intro statistics in important ways. All three aim to keep the mathematics and statistics strongly connected to biology. The panelists describe their different approaches to teaching statistics for biology and discuss how and why an integrated approach gives advantages. Important issues are how to tie statistics advantageously with calculus, how to keep "advanced" mathematical and statistical topics accessible to introductory-level biology students, and how to employ computation productively. The discussion contrasts a comprehensive "team" approach (at ETSU) with stand-alone courses (at Macalester and at OSU) and refers to the institutional opportunities and constraints that have shaped the programs at their different institutions.

In this module, students can test their knowledge of levels of measurement by attempting to determine the the level of measurement of ten different variables. For each variable, a statement is also provided and students can indicate whether the statement about the variable is valid or invalid (given the way in which the variable was measured). There is also a brief "refresher" included here about levels of measurement.

A joke to introduce the idea of asymptotic distributions. The joke was written by Dennis Pearl of The Ohio State University.