Lecture Examples

  • This site includes several short tutorials that showcase different features of JMP 7. There is also another site with JMP tutorials at http://stat.fsu.edu/tutorials/
    0
    No votes yet
  • Mathematics alone make us feel the limits of our intelligence. For we can always suppose in the case of an experiment that it is inexplicable because we don't happen to have all the data. In mathematics we have all the data and yet we don't understand. is a quote by French philosopher and political activist Simone Weil (1909-1943). The quote may be found on page 511 of the second volume of "Simone Weil's Notebooks" first published in English in 1956 (translated by Arthur Willis).
    0
    No votes yet
  • November 23, 2010 Activity Webinar presented by Stacey Hancock, Reed College, Jennifer Noll, Portland State University, Sean Simpson, Westchester Community College, and Aaron Weinberg, Ithaca College, and hosted by Leigh Slauson, Capital University. Extra materials available for download free of charge. Many instructors ask students to demonstrate the frequentist notion of probability using a simulation early in an intro stats course. Typically, the simulation involves dice or coins, which give equal (and known) probabilities. How about a simulation involving an unknown probability? This webinar discusses an experiment involving rolling (unbalanced) pigs. Since the probabilities are not equal, this experiment also allows the instructor to have students think about the concept of fairness within games.

    0
    No votes yet
  • October 26, 2010 Activity Webinar presented by Tisha Hooks, Winona State University and hosted by Leigh Slauson, Capital University. Extra materials available to download free of charge. The purpose of this webinar is to introduce an activity to enhance students' understanding of various descriptive measures. In particular, by completing this hands-on activity students will experience a visual interpretation of a mean, median, outlier, and the concept of distance-to-mean.
    0
    No votes yet
  • August 24, 2010 Activity Webinar presented by Jackie Miller, The Ohio State University and hosted by Leigh Slauson, Capital University. Extra materials available for download free of charge. When Dr. Miller took a graduate course in College Teaching, she 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. Dr. Miller recalls bringing the idea back with her to one of the OSU elementary statistics courses where it has been used successfully since 1996. Recently a graduate teaching assistant (GTA) suggested to other GTAs that this might be good in another 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, the webinar presents 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 Dr. Miller was to learn about the jigsaw method and will want to try it in your classroom.
    0
    No votes yet
  • A cartoon for use in discussions about the value of using a placebo in an experiment (especially if the results are to be analyzed using a t-test). The cartoon is the work of Theresa McCracken and appears as #6864 on McHumor.com Free for non-profit use in statistics course such as in lectures and course websites.
    0
    No votes yet
  • This issue contains an article that provides an example of a paired samples test related to flying and gliding. It also includes an article about understanding confounding from lurking variables using graphs. Other articles include: a short description about what the t-tests actually tests, an interview with David Moore about why 30 is the "magic" number, a discussion about whether or not outliers should be deleted from a data set, a discussion of observational studies, and a simulation piece about random numbers from non-random arithmetic.
    0
    No votes yet
  • This issue contains an interview with Sallie Keller-McNulty and an article about which came first -- the chicken or the egg. Other articles include a discussion related to an AP Statistics example of seeing the trees for the forest (this focuses on understanding variability between groups and within groups), a discussion of how high r can go, a simulation piece focused on shrinking students, poisoned children, and bootsraps, and an example of a permutation test of the Challenger O-Ring data.
    0
    No votes yet
  • A sketch by Anastasia Mandel reinterpreting Government Bureau by George Tooker (1956) with the statistical caption "Queuing theory and implementation." This is part of a collection of sketches by Anastasia Mandel and their accompanying statistical captions discussed in the paper "How art helps to understand statistics" (Model Assisted Statistics and Applications, 2009) by Stan Lipovetsky and Igor Mandel in volume 4 pages 313-324. Free to use in classrooms and on course websites.
    0
    No votes yet
  • A sketch by Anastasia Mandel reinterpreting Fortune-Teller by Michail Vrubel (1895) with the statistical caption "It helps when other statistical techniques fail." This is part of a collection of sketches by Anastasia Mandel and their accompanying statistical captions discussed in the paper "How art helps to understand statistics" (Model Assisted Statistics and Applications, 2009) by Stan Lipovetsky and Igor Mandel in volume 4 pages 313-324. Free to use in classrooms and on course websites.
    0
    No votes yet

Pages

register