A cartoon to provide a nice avenue for facilitating discussions of power in significance testing.The cartoon was used in the November, 2017 CAUSE cartoon caption contest and the winning caption was written by John Dawson from Texas Tech 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 to provide a nice avenue for facilitating discussions of planning for adequate sample sizes in experiments.The cartoon was used in the October, 2017 CAUSE cartoon caption contest and the winning caption was written by Greg Snow from Grigham Young 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 light bulb joke that can be used in discussing how the choice of model might affect the conclusions drawn. The joke was submitted to AmStat News by Robert Weiss from UCLA and appeared on page 48 of the October, 2018 edition.
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.
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 overs the following: covariance patterns and generalized estimating equations (GEE).
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture overs the following: conditional logistic regression, conditional likelihood for matched pairs, the non-central hypergeometric, the conditional maximum likelihood estimator (CMLE), conditional confidence interval for odds ratios, and McNemar's statistic.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: maximum likelihood estimation for logistic regression, sample size requirements for approximate normality of the MLE’s, confidence intervals, likelihood ratio statistic, score test statistic, deviance, Hosmer-Lemeshow goodness-of-fit statistic, the Hosmer-Lemeshow statistic, parameter estimates, scaled/unscaled estimates, residuals, grouped binomials, and model building strategies.
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 covers the following: 2x2 contingency tables, fixing columns and rows, MLE, and previous topics within the context of contingency tables (variance, confidence intervals, standard error approximation, likelihood ratio, etc.).