A cartoon to instigate discussions on the use of random numbers in both designing and analyzing data.The cartoon was used in the October 2018 CAUSE cartoon caption contest and the winning caption was written by Anthony Bonifonte from Denison University. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.
A cartoon to be used in class discussions that introduce basic queueing theory. The cartoon was used in the August, 2017 CAUSE cartoon caption contest and the winning caption was submitted by Larry Lesser from The University of Texas at El Paso. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.
A cartoon to aid in discussing Simpson's paradox by providing an illustration that an association seen in smaller groups can reverse direction when the data are aggregated. The cartoon was drawn by Britsh cartoonist John Landers based on idea from Dennis Pearl of Penn State University.
A cartoon to be used as a vehicle to discuss Cornfield’s simple conditions required of a potential confounder to create a Simpson's Paradox situation The cartoon was used in the June 2018 CAUSE Cartoon Caption Contest. This winning caption was submitted by Larry Lesser from The University of Texas at El Paso. The drawing was created by British cartoonist John Landers based on an idea from Dennis Pearl of Penn State University.
A cartoon suitable for use in teaching about Bayes Theorem (an obvious follow-up exercise is to ask what “P(C)” would have to be to make the “Modified Bayes Theorem” correct). The cartoon is number 2059 from the webcomic series at xkcd.com created by Randall Munroe. Free to use in the classroom and on course web sites under a creative commons attribution-non-commercial 2.5 license.
A cartoon to be used for in discussing the Poisson model for the number of rare events in a fixed amount of time. The cartoon was used in the August 2018 CAUSE Cartoon Caption Contest. This caption received an honorable mention. The drawing was created by British cartoonist John Landers based on an idea from Dennis Pearl of Penn State University. The winning caption in the August competition was "Always read the full informed consent document before signing up to be in a matched-pairs experiment," written by Greg Snow from Brigham Young University and may be found at https://www.causeweb.org/cause/resources/fun/cartoons/twins
A joke to help in discussing the Geometric and Hypergeometric probability distributions. A version of the joke was submitted to AmStat News by Sara Venkatraman, a student at Cornell University and appeared in the October, 2018 issue. The joke was modified to relate the hypergeometric distribution to sampling without replacement by the CAUSEweb fun collection editors (Dennis Peaaerl and Larry Lesser).
A song about the Problem of Points, whose discussion in the 17th century led to the foundations of probability theory and expected value. The lyric was written in 2017 by Lawrence M Lesser from The University of Texas at El Paso and may be sung tot he tune of the Sting #1 1983 Grammy-winning hit “Every Breath You Take”.
Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and logical analysis method aimed at identifying and assessing risks in complex technological systems for the purpose of cost-effectively improving their safety and performance. NASA’s objective is to better understand and effectively manage risk, and thus more effectively ensure mission and programmatic success, and to achieve and maintain high safety standards at NASA. This PRA Procedures Guide, in the present second edition, is neither a textbook nor an exhaustive sourcebook of PRA methods and techniques. It provides a set of recommended procedures, based on the experience of the authors, that are applicable to different levels and types of PRA that are performed for aerospace applications.
This NASA-HANDBOOK is published by the National Aeronautics and Space Administration (NASA) to provide a Bayesian foundation for framing probabilistic problems and performing inference on these problems. It is aimed at scientists and engineers and provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models. The overall approach taken in this document is to give both a broad perspective on data analysis issues and a narrow focus on the methods required to implement a comprehensive database repository.