A cartoon suitable for use in teaching about how the precision of measurements propagate when mathematical operations are applied to them . The cartoon is number 295 (April, 2020) 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.
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 presentation was given by Aneta Siemiginowska at the 4th International X-ray Astronomy School (2005), held at the Harvard-Smithsonian Center for Astrophysics in Cambridge, MA.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: the poisson log-linear model, poisson regression, estimated rate ratio, and negative binomial distribution.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: generalized IxJ contingency tables, degrees of freedom, Fisher's exact test, and generalized odds ratio.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture provides a review of probability and statistical concepts such as conditional probabilities, Bayes Theorem, sensitivity and specificity, and binomial and poisson distributions.
This is a graduate level introduction to statistics including topics such as probabilty/sampling distributions, confidence intervals, hypothesis testing, ANOVA, and regression. Perfect for students and teachers wanting to learn/acquire materials for this topic.
The objective of this course is to learn and apply statistical methods for the analysis of data that have been observed over time. Our challenge in this course is to account for the correlation between measurements that are close in time. Perfect for students and teachers wanting to learn/acquire materials for this topic.
The focus of this class is a multivariate analysis of discrete data. We will learn basic statistical methods and discuss issues relevant for the analysis of some discrete distribution, cross-classified tables of counts, (i.e., contingency tables), success/failure records, questionnaire items, judge's ratings, etc. Being familiar with matrix algebra is helpful in completing this course. Perfect for students and teachers wanting to learn/acquire materials for this topic.
A collection of Java applets and simulations covering a range of topics (descriptive statistics, confidence intervals, regression, effect size, ANOVA, etc.).