This is an online calculator that can be used to determine the recommended sample size that is needed for a specific margin of error, confidence level, and population size.
This is an online calculator that can be used to determine the recommended sample size that is needed for a specific margin of error, confidence level, and population size.
March 24, 2009 Activity webinar presented by Nicholas Horton, Smith College, and hosted by Leigh Slauson, Otterbein College. Students have a hard time making the connection between variance and risk. To convey the connection, Foster and Stine (Being Warren Buffett: A Classroom Simulation of Risk and Wealth when Investing in the Stock Market; The American Statistician, 2006, 60:53-60) developed a classroom simulation. In the simulation, groups of students roll three colored dice that determine the success of three "investments". The simulated investments behave quite differently. The value of one remains almost constant, another drifts slowly upward, and the third climbs to extremes or plummets. As the simulation proceeds, some groups have great success with this last investment--they become the "Warren Buffetts" of the class. For most groups, however, this last investment leads to ruin because of variance in its returns. The marked difference in outcomes shows students how hard it is to separate luck from skill. The simulation also demonstrates how portfolios, weighted combinations of investments, reduce the variance. In the simulation, a mixture of two poor investments is surprisingly good. In this webinar, the activity is demonstrated along with a discussion of goals, context, background materials, class handouts, and references (extra materials available for download free of charge)
When performing a hypothesis test about the population mean, a possible reason for the failure of rejection of the null hypothesis is that there's an insufficient sample size to achieve a powerful test. Using a small data set, Minitab is used to check for normality of the data, to perform a 1-Sample t test, and to compute Power and Sample Size for 1-Sample t.
Big data analysis is explained in this online course that introduces the user to the tools Hadoop and Mapreduce. These tools allow for the parallel computing necessary to analyze large amounts of data.
A joke to start a discussion on joint probability distributions. The joke was written in 2018 by Larry Lesser from The University of Texas at El Paso.
"There are a lot of small data problems that occur in big data. They don't disappear because you've got lots of stuff. They get worse." is a quote by British biostatistician David J. Spiegelhalter (1953 - ). The quote may be found in a March 28, 2014 article in the Financial Times written by Tim Hartford entitled "Big data: are we making a big mistake?"
A joke to aid in discussing probability density functions for continuous random variables. The joke was written in 2016 by Judah Lesser an AP statistics student from El Paso, Texas.
A joke for discussing how transformations can make data more normal and stabilize variances across groups with different means (here the square root transformation for Poisson data). The joke was written in 2016 by Larry Lesser from The University of Texas at El Paso.
A joke to be used in teaching about the use of randomization in experiments or about the Pearson correlation coefficient. The idea for the joke came from Lawrence Mark Lesser of The University of Texas at El Paso in 2012.
A pun to familiarize students with Anscombe's Quartet - the group of 4 data sets with the same means, standard deviations, correlations, and regression lines for X and Y that were produced by British statistician Frank Anscombe in a 1973 paper in the American Statistician. The joke was written in 2016 by Larry Lesser from The University of Texas at El Paso. This joke should be used in a written rather than oral presentation since students will not "get" the joke if they have never heard of Anscombe's Quartet - the value for teaching coming from having them look it up.