Algebra level symbolic math

  • Take nothing on its looks; take everything on evidence. There's no better rule. is a quote by English novelist Charles Dickens (1812 - 1870). The quote appears in chapter 40 of his popular novel "Great Expectations" written as a weekly serial from December 1860 to August 1861. The line was spoken in the novel by Mr. Jaggers to Pip.
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  • Whatever the progress of human knowledge, there will always be room for ignorance, hence for chance and probability. is a quote by French mathematician Emile Borel (1871 - 1956). The quote may be found on page 12 of his 1914 book "Le hasard"
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  • The world of science lives fairly comfortably with paradox. We know that light is a wave and also that light is a particle. The discoveries made in the infinitely small world of particle physics indicate randomness and chance, and I do not find it any more difficult to live with the paradox of a universe of randomness and chance and a universe of pattern and purpose than I do with light as a wave and light as a particle. Living with contradiction is nothing new to the human being is a quote by American young adult fiction author Madeline L'Engle (1918-2007). The quote is on page 125 of her 1988 book "Two-Part Invention: The Story of a Marriage".
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  • 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).
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  • 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.

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  • 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.
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  • September 28, 2010 Activity webinar presented by Carolyn Cuff, Westminster College and hosted by Leigh Slauson, Capital University. Extra materials available for download free of charge. 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.
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  • 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.
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  • July 27, 2010 Activity Webinar presented by Herle McGowan, North Carolina State University and hosted by Leigh Slauson, Capital University. Extra materials available for download free of charge. In this webinar, the webinar discusses 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.
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  • 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.
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