This song covers some major real-world examples in the history of random sampling. The lyric was written in 2017 by Lawrence M Lesser from The University of Texas at El Paso and can be sung to the tune of theHarry Casey and Richard Finch song “(Shake, Shake, Shake) Shake Your Booty” that was a 1976 #1 hit for KC and the Sunshine Band. Audio of the parody was produced and sung by students in the commercial music program of The University of Teas at El Paso.
Explore the Hubble Deep Fields from a statistical point of view. Watch out for the booby traps of bias, the vagueness of variability, and the shiftiness of sample size as we travel on a photo safari through the Hubble Deep Fields (HDFs).
This resource provides information on all of the astronauts that have been a part of the U.S. space program (as well as some facts about other countries' space programs), including how many flights each has participated in, where they are from, where they attended college, and many more fun facts. This material contacts a great deal of data on these individuals and could be used as data sets for teaching basic statistics concepts.
The information in this resource provides an overview of ISS utilization up to the end of March 2013.
The purpose of this work is to provide a comprehensive reference for facts about Project Apollo, America’s effort to put humans on the Moon. While there have been many studies recounting the history of Apollo, this new book in the NASA History Series seeks to draw out the statistical information about each of the flights that have been long buried in numerous technical memoranda and historical studies. It seeks to recount the missions, measuring results against the expectations for them.
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.
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 article gives a brief overview of the role of a biostatistician at NASA. It also provides names of those one can contact in this area.