George Cobb, Mount Holyoke College
Tuesday, October 12, 2010 - 2:00pm
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.
Thomas Moore, Grinnell College
Tuesday, September 14, 2010 - 2:00pm
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.
Diane Fisher, University of Louisiana at Lafayette; Jennifer Kaplan, Michigan State University; and Neal Rogness, Grand Valley State University
Tuesday, August 10, 2010 - 2:00pm
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.
Webster West, Texas A&M University
Tuesday, July 13, 2010 - 2:00pm
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.
Lynette Hoelter, University of Michigan
Tuesday, June 8, 2010 - 2:00pm
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.
Tuesday, May 11, 2010 - 2:00pm
This webinar will present data, tools, materials and the pedagogical approach of the Statistics Online Computational Resource (SOCR) for technology-enhanced probability and statistics education. Following a review of the different types of SOCR online resources, we will go over two specific classroom utilization examples. The first one provides a hands-on demonstration of a statistical concept (CLT) using interactive virtual experiments and simulations. The second example will showcase the use of SOCR resources to address interesting social, health, environmental, scientific, and engineering challenges. In this case, we'll focus on the Ozone pollution in California, formulate health-related hypotheses, identify appropriate data and employ web-based exploratory and statistical data analysis tools.
What is www.SOCR.ucla.edu?
The Statistics Online Computational Resource provides portable online aids for probability and statistics education, technology based instruction and statistical computing. SOCR tools and resources include a repository of interactive applets, computational and graphing tools, instructional and course materials.
SOCR aims to develop new Java applets, design diverse extensible SOCR learning activities, develop XML/HTML navigation/search tools for interactive materials, and validate and assess technology-enhances pedagogical techniques.
SOCR Products
Tools/Applets: Distributions, Experiments, Analyses, Games, Modeler & Graphs.
Multilingual instructional resources: EBooks, continuing statistics education workshops/seminars
Learning activities: interactive, data-driven and technology-enhanced learning activities
Examples:
Central Limit Theorem
Hands-on California Ozone Data Activity
Data: Diverse publicly accessible datasets for copy-paste/download utilization
Example: Latin Letters Frequency Distribution
Dissemination: papers, conferences, workshops, etc.
SOCR Evaluation and Efficacy
We have conducted several control-based studies of the efficacy of technology-enhanced statistics education. Using IRB-approved studies, quantitative and qualitative measures of student performance were recorded in classes using traditional (control) instruction (R or Stata based) and classes using SOCR resources and tools. Non-parametric analyses of the data showed very statistically significant (SOCR) treatment effects (p < 10-4) on student performance and perception of the material. The practical significance of these treatment effects were more modulated. More details about these studies are available here.
Summary
Main SOCR server, applets
Data, activities and EBooks
Feedback and Forum
Graphical SOCR Navigator
Jeanne Albert & Bill Peterson, Middlebury College
Tuesday, April 13, 2010 - 2:00pm
This year, Jeanne and Bill assumed co-editorship of the Chance News Wiki, which as of March 15 will be moving to CAUSEweb. The Wiki provides reviews of current news stories that are relevant to teaching statistics and probability, along with links to original articles and related resources. This webinar will describe the various ways that Chance project materials have been used, in areas ranging from traditional introductory statistics to statistical literacy courses to first-year seminars. We will also discuss the mechanics of posting to the Wiki, and hope to inspire some new contributors.
Hollylynne Stohl Lee, North Carolina State University
Tuesday, April 6, 2010 - 2:00pm
This is a CAUSE Special Presentation for USCOTS Research Cluster members.
Dalene Stangl, Duke University
Tuesday, March 9, 2010 - 2:00pm
During the past 20 years, undergraduate education has shifted from student as passive recipient of information to student as active participant in the classroom. I wrote an article for Chance magazine's 20th anniversary issue titled, "Using Chance to Engage Undergraduates in the Study of Statistics." The article gave examples of activities inspired by Chance magazine articles from the last 20 years. This webinar will take articles from a recent issue of Chance and demonstrate the ease with which any issue can be used to develop class activities that are fun for high school students and undergraduates whether the course is a basic quantitative literacy course, an AP statistics course, an introductory course for non-statistics majors, or a core or elective course for the statistics major.
Hollylynne Stohl Lee, North Carolina State University; and Todd Lee, Elon University
Tuesday, February 9, 2010 - 2:00pm
A model for probabilistic reasoning will be discussed that may support students' statistical reasoning. The development of the model and instructional implications are based on theoretical considerations and empirical results from work with middle grades students. Significant time for discussion is planned to get reactions to the model as well as to discuss aspects of probability that participants believe are foundational to building statistical literacy or reasoning.