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  • This Haiku was written by Dr. Nyaradzo Mvududu of the Seattle Pacific University School of Education. The poem took first place in the 2007 A-Mu-sing competition.
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  • This activity illustrates the convergence of long run relative frequency to the true probability. The psychic ability of a student from the class is studied using an applet. The student is asked to repeatedly guess the outcome of a virtual coin toss. The instructor enters the student's guesses and the applet plots the percentage of correct answers versus the number of attempts. With the applet, many guesses can be entered very quickly. If the student is truly a psychic, the percentage correct will converge to a value above 0.5.
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  • This limerick was written by Dr. Nyaradzo Mvududu of the Seattle Pacific University School of Education. The poem was given an honorable mention in the 2007 A-Mu-sing competition.
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  • The purpose of this activity is 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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  • A cartoon about the perception that statistics exams are difficult. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.
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  • By means of a simple story and a worksheet with questions we guide the students from research question to arriving at a conclusion. The whole process is simply reasoning, no formulas. We use the reasoning already done by the student to introduce the standard vocabulary of testing statistical hypotheses (null & alternative hypotheses, p-value, type I and type II error, significance level). Students need to be familiar with binomial distribution tables. After the ducks story is finished, the class is asked to come up with their own research question, collect the data, do the hypotheses testing and answer their own research question. The teaching material is intended to be flexible depending of the time available. Instructors can choose to do just the interactive lecture type, interactive lecture + activity, or even add the optional material.
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  • 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.
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  • This hands-on activity is appropriate for a lab or discussion section for an introductory statistics class, with 8 to 40 students. Each student performs a binomial experiment and computes a confidence interval for the true binomial probability. Teams of four students combine their results into one confidence interval, then the entire class combines results into one confidence interval. Results are displayed graphically on an overhead transparency, much like confidence intervals would be displayed in a meta-analysis. Results are discussed and generalized to larger issues about estimating binomial proportions/probabilities.
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  • 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.
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  • In this hands-on activity, students count the number of chips in cookies in order to carry out an independent samples t-test to see if Chips AhoyŒ¬ cookies have a higher, lower, or different mean number of chips per cookie than a supermarket brand. First there is a class discussion that can include concepts about random samples, independence of samples, recently covered tests, comparing two parameters with null and alternative hypotheses, what it means to be a chip in a cookie, how to break up the cookies to count chips, and of course a class consensus on the hypotheses to be tested. Second the students count the number of chips in a one cookie from each brand, and report their observations to the instructor. Third, the instructor develops the independent sample t-test statistic. Fourth, the students carry out (individually or as a class) the hypothesis test, checking the assumptions on sample-size/population-shape.
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