Faculty

  • A cartoon to teach about the capture-recapture method. Cartoon by John Landers (www.landers.co.uk) based on an idea and sketch from Sheila O. Weaver (University of Vermont). This is part of a three cartoon set from Dr. Weaver that took first place in the cartoon category of the 2007 A-Mu-sing competition. Free to use in the classroom and on course web sites.

    0
    No votes yet
  • A cartoon to teach about the capture-recapture method to estimate population size. Cartoon by John Landers (www.landers.co.uk) based on an idea and sketch from Sheila O. Weaver (University of Vermont). This is part of a three cartoon set that took first place in the cartoon category of the 2007 A-Mu-sing competition. Free to use in the classroom and on course web sites.

    4
    Average: 4 (1 vote)
  • A cartoon to teach about the family of t-distributions including their relationship to the normal distribution. Cartoon by John Landers (www.landers.co.uk) based on an idea and sketch from Sheila O. Weaver (University of Vermont). This is part of a three cartoon set that took first place in the cartoon category of the 2007 A-Mu-sing competition. Free to use in the classroom and on course web sites.

    5
    Average: 5 (1 vote)
  • A series of 19 songs used to teach Structural Equations Modeling (SEM) by Alan Reifman of Texas Tech University. A video of an in-class performance of the musical on April 27, 2007, is also available at the website. The Musical took second place in the 2007 A-Mu-sing competition.
    0
    No votes yet
  • This pdf text file gives a short introduction to the methods of Bayesian inference. It gives a simple example that deals with jumping a paper frog. The topics listed in this document include: An example, comparison of frequentist and Bayesian methods, credible vs. confidence intervals, choice of prior and its effect on the posterior distribution.
    0
    No votes yet
  • Statz 4 Life is a 5 minute, 13 second video that provides a fun review of statistical inference topics (for example, the theme of examining observed differences in the numerator and error in the denominator). The video was first shown on May 18, 2006 in Chuck Tate's research methods course, while he was a graduate student in the Department of Psychology at the University of Oregon. The rappers are (in order of appearance): Jeph Loucks, Chuck Tate, Chelan Weaver, and Cara Lewis. Jennifer Simonds provides the singing talent. Credits: Concept, lyrics, and cinematography by Chuck Tate, audio mixing by Jeph Loucks, and video editing by Chuck Tate and Jeph Loucks. The background beat is Nelly's song "Grillz," of which this video is a parody.

    0
    No votes yet
  • Statistic Acrostic is a poem by statistics educator Lawrence Mark Lesser and biostatistician Dennis K. Pearl that covers several statistical concepts using only 26 words (one starting with each letter of the alphabet). It was written in 2008 as a response to an example and challenge from JoAnne Growney in her poem “ABC, an Analytic Geometry Poem” in a 2006 article in Journal of Online Mathematics and Its Applications.  To expand the usefulness of this form for educational objectives, a teacher could have students not follow the 26-letter alphabet, but generate an acrostic from a statistics word or phrase.

    0
    No votes yet
  • The t-distribution activity is a student-based in-class activity to illustrate the conceptual reason for the t-distribution. Students use TI-83/84 calculators to conduct a simulation of random samples. The students calculate standard scores with both the population standard deviation and the sample standard deviation. The resulting values are pooled over the entire class to give the simulation a reasonable number of iterations. This document provides the instructor with learning objectives, context, mechanics, follow-up, and evidence from use associated with the in-class activity.
    0
    No votes yet
  • Funded by the National Science Foundation, workshops were held over a three-year period, each with about twenty participants nearly equally divided between mathematics educators and statisticians. In these exchanges the mathematics educators presented honest assessments of the status of mathematics education research (both its strengths and its weaknesses), and the statisticians provided insights into modern statistical methods that could be more widely used in such research. The discussions led to an outline of guidelines for evaluating and reporting mathematics education research, which were molded into the current report. The purpose of the reporting guidelines is to foster the development of a stronger foundation of research in mathematics education, one that will be scientific, cumulative, interconnected, and intertwined with teaching practice. The guidelines are built around a model involving five key components of a high-quality research program: generating ideas, framing those ideas in a research setting, examining the research questions in small studies, generalizing the results in larger and more refined studies, and extending the results over time and location. Any single research project may have only one or two of these components, but such projects should link to others so that a viable research program that will be interconnected and cumulative can be identified and used to effect improvements in both teaching practice and future research. The guidelines provide details that are essential for these linkages to occur. Three appendices provide background material dealing with (a) a model for research in mathematics education in light of a medical model for clinical trials; (b) technical issues of measurement, unit of randomization, experiments vs. observations, and gain scores as they relate to scientifically based research; and (c) critical areas for cooperation between statistics and mathematics education research, including qualitative vs. quantitative research, educating graduate students and keeping mathematics education faculty current in education research, statistics practices and methodologies, and building partnerships and collaboratives.

    0
    No votes yet
  • This applet generates confidence intervals for means or proportions. The options for confidence intervals for means include "z with sigma," "z with s," or "t." The options for confidence intervals for proportions are "Wald," "Adjusted Wald," or "Score." Users set the population parameters, sample size, number of intervals, and confidence level. Click "Sample," and the applet will graph the intervals. Intervals shown in green contain the true population mean or proportion, while intervals in red do not. The true mean or proportion is shown by a blue line. The applet displays the proportion of intervals containing the population parameter for each sample and a running total of all the samples. Users can also click on a particular interval to display the numerical interval or sort the displayed confidence intervals from smallest to largest. This applet is part of a collection designed to accompany the textbook "Investigating Statistical Concepts, Applications, and Methods" (ISCAM) and is used in Exploration 4.3 on page 327, Investigation 4.3.6 on page 331, and Exploration 4.4 on page 350. This applet also supplements "Workshop Statistics: Discovery with Data," 2nd edition, Activity 19-5 on page 403. Additional materials written for use with these applets can be found at http://www.mathspace.com/NSF_ProbStat/Teaching_Materials/rowell/final/16_cireview_bc322_2.doc and http://www.mathspace.com/NSF_ProbStat/Teaching_Materials/rowell/final/15_sampdistreview_bc322_1.doc.
    0
    No votes yet

Pages