A cartoon with a neat double pun that can be a nice vehicle to discuss how the expectations of non-linear functions of a random variable is not the same as the function of the expectations. The cartoon was used in the February 2019 CAUSE cartoon caption contest and the winning caption was written by Joseph Gerda from College of the Canyons. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.
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 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 presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: unconditional likelihood, elimination of nuisance parameters, and Mantel-Haenzsel estimate.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: linear association, correlation coefficient, ridits/modified ridits, nonparametric methods, Cochran-Armitage Trend test,
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.
Those who complete this course will be able to select appropriate methods of multivariate data analysis, given multivariate data and study objectives; write SAS and/or Minitab programs to carry out multivariate data analyses; and interpret results of multivariate data analyses. Perfect for students and teachers wanting to learn/acquire materials for this topic.
This text explains the differences between t-tests, z-tests, tests with proportions, and tests of correlation.