An application of Bayes Theorem that performs the same calculations for the situation where the several probabilities are constructed as indices of subjective confidence.
An application of Bayes Theorem that performs the same calculations for the situation where the several probabilities are constructed as indices of subjective confidence.
This page will generate a graphic and numerical display of the properties of a binomial sampling distribution, for any values of p and q, and for values of n between 1 and 40, inclusive.
The page will calculate the following: Exact binomial probabilities, Approximation via the normal distribution, Approximation via the Poisson Distribution. This page will calculate and/or estimate binomial probabilities for situations of the general "k out of n" type, where k is the number of times a binomial outcome is observed or stipulated to occur, p is the probability that the outcome will occur on any particular occasion, q is the complementary probability (1-p) that the outcome will not occur on any particular occasion, and n is the number of occasions.
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.
November 14, 2006 webinar presented by Chrstine Franklin, University of Georgia, and Jessica Utts, University of California and hosted by Jackie Miller, The Ohio State University. In 2005 the American Statistical Association endorsed the recommendations of a report written by leading statistics educators, called "Guidelines for Assessment and Instruction in Statistics Education" (GAISE). The report had two parts - one for K-12 and one for the college introductory statistics course. In this webinar, two members of the report-writing team review the recommendations in the report, and provide suggestions for how to begin to implement them.
A cartoon to teach the idea that sampling variability depends on the size of the sample, and not on the size of the population (as long as the sample is a small part of the population). Cartoon drawn by British cartoonist John Landers based on an idea from Dennis Pearl. Free to use in the classroom and for course websites.
The idea that the examination of a relatively small number of randomly selected individuals can furnish dependable information about the characteristics of a vast unseen universe is an idea so powerful that only familiarity makes it cease to be exciting Is a quote from American Educational Statistician Helen Mary Walker (1891 - 1983). Helen Walker was the first women to serve as the president of the American Statistical Association and this quote is from her December 27, 1944 presidential address at the 104th annual meeting of the ASA in Washington, D.C. The full address may be found in the "Journal of the American Statistical Association" (1945; vol. 40, #229 p. 1-10).
A song to be used in discussing the value of random selection in sampling and random assignment in experimentation. The lyrics were written by Mary McLellan from Aledo High School in Aledo, Texas as one of several dozen songs created for her AP statistics course. The song may be sung to the tune of the 2014 hit “All About that Bass,” by Meghan Trainor. Also, an accompanying video may be found at https://www.youtube.com/watch?v=br-5FtoYfkc
The program DistCalc calculates probabilities and critical values for the most important distributions. The purpose of this program is to show the concept of critical values and the replacement of printed distribution tables. The Distribution Calculator offers calculations for the normal distribution, the t distribution, the chi-square distribution, and the F distribution.
This applet allows the user to simulate a race where the results are based on the roll of a die. For each outcome of the die, the user chooses which player moves forward. Then that car moves forward the given number of spaces. Users can experiment with the race by determining which player will win more often based on the rules that they specify.