Resource Library

A cartoon to illustrate the importance of statistical principles and probability models in internet search engines.  The cartoon was drawn in 2013 by British cartoonist John Landers based on an idea by Dennis Pearl from Ohio State University.  This item is part of the cartoons and readings from the “World Without Statistics” series that provided cartoons and readings on important applications of statistics created for celebration of 2013 International Year of Statistics.  The series may be found at https://online.stat.psu.edu/stat100/lesson/1/1.4

A cartoon useful in applied probability courses to discuss the nature of actuarial work and the importance of accounting for rare events.The cartoon was used in the April, 2018 CAUSE cartoon caption contest and the winning caption was written by Larry Lesser from The University of Texas at El Paso.  An alternative caption that was a co-winner in that month’s contest was "Open your eyes to catch the significant events occurring at the tails," submitted by Debmalya Nandy, a graduate student at Penn State 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 that can be used in discussing the effect of outliers – especially on significance testing. The cartoon was used in the April, 2018 CAUSE cartoon caption contest and the winning caption was submitted by Debmalya Nandy, a graduate student at Penn State University.  An alternative caption that was a co-winner in that month’s contest was "Actuaries write umbrella policies to cover freak accidents" written 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 that can be a vehicle to discuss how interesting discoveries are often made by investigating outliers.The cartoon was used in the March 2018 CAUSE cartoon caption contest and the winning caption was written by Jim Alloway from EMSQ Associates. 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 that can provide a nice avenue for facilitating discussions of the importance of having a plan to clean dirty/messy data.The cartoon was used in the February 2018 CAUSE cartoon caption contest and the winning caption was written by Jennifer Ann Morrow from University of Tennessee. 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 that can be helpful in discussing how computational advances affect the processing and analysis of big data. The cartoon was used in the November 2018 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.

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. 

Dr. Kuan-Man Xu from the NASA Langley Reserach Center writes, "A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries-Matusita distance and the Kuiper distance. "

This paper comes from researchers at the NASA Langley Research Center and College of William & Mary.  

"The experience of retinex image processing has prompted us to reconsider fundamental aspects of imaging and image processing. Foremost is the idea that a good visual representation requires a non-linear transformation of the recorded (approximately linear) image data. Further, this transformation appears to converge on a specific distribution. Here we investigate the connection between numerical and visual phenomena. Specifically the questions explored are: (1) Is there a well-defined consistent statistical character associated with good visual representations? (2) Does there exist an ideal visual image? And (3) what are its statistical properties?"

This lesson introduces students to creating spreadsheets for statistical analysis.