A cartoon that can help in discussing how context matters in thinking about trend and "Seasonal" patterns in time series.The cartoon was used in the July 2018 CAUSE cartoon caption contest and the winning caption was written by Karsten Luebke from FOM University in Germany. The cartoon was drawnby British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.
A cartoon to start a discussion on the importance of appropriate axis labels. The cartoon was used in the September, 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. Another caption noticed the lack of any scale on the charts read simply "Label your axes!" and was submitted by Kyle Falbo of the College of the Redwoods. A different use of the cartoon can be made with the caption "Looks like a bad case of Regression to the Mean," which might be used in discussing that topic since the sicker patient in the cartoon is improving more.
A cartoon to initiate a class discussion about the idea of using statistical methods to navigate data and draw inferences. The cartoon was used in the July, 2017 CAUSE cartoon caption contest and the winning caption was submitted by Debmalya Nandy, a graduate student at Penn State University. An alternative caption that took an honorable mention in that month's contest was "Check that variances are equal before diving in with pooled variance!" 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 song to help students remember the empirical rule that it is rare to see an observation more than three sd's away from the mean, while about 19 out of 20 will fall within two sd's and about 2 out of 3 within one sd. The lyrics were weritten in 2017 by Lawrence M Lesser from The University of Texas at El Paso and may be sung to the tune of "Material Girl" written by Peter Brown and Robert Rans and populartized by Madonna. Audio of the parody was produced by Nicolas Acedo Aguilar and sung by Alexandria Campos, students in the commercial music program of The University of Texas at El Paso.
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 resource was prepared to give the practicing engineer a clear understanding of probability and statistics with special consideration to problems frequently encountered in aerospace engineering. It is conceived to be both a desktop reference and a refresher for aerospace engineers in government and industry. It could also be used as a supplement to standard texts for in-house training courses on the subject.
These pages explain the following basic statistics concepts: mean, median, mode, variance, standard deviation and correlation coefficient (with example from the Institute on Climate and Planets).
This lesson introduces students to creating spreadsheets for statistical analysis.