A cartoon to teach ideas of conditional probability. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University) in 2008. Free to use in the classroom and on course web sites.
A cartoon to teach ideas of conditional probability. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University) in 2008. Free to use in the classroom and on course web sites.
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.
A cartoon to teach about the need to think carefully about the assumptions underlying a statistical model (also uses the idea that you can multiply chances for independent events to find the chance that they all occur). Drawn by British cartoonist John Landers based on an idea from Dennis earl. Free to use in the classroom and on course websites.
A song about the important contributions of Karl Pearson, Charles Spearmen, William S. Gosset, and Ronald Fisher. Lyrics written by Nyaradzo Mvududu from Seattle Pacific University. May sing to the tune of John Lennon's 1971 song "Imagine." The lyrics were awarded third place in the song category of the 2011 CAUSE A-Mu-sing competition. Musical accompaniment realization are by Joshua Lintz and vocals are by Mariana Sandoval from University of Texas at El Paso.
A Haiku about the meaning of significance by Dr. Nyaradzo Mvududu of the Seattle Pacific University School of Education. The poem was awarded a tie for second place in the 2011 CAUSE A-Mu-sing competition.
A poem useful in teaching aspects about hypothesis testing, especially the caveat that unimportant differences may be deemed significant with a large sample size. The poem was written by Mariam Hermiz, a student at University of Toronto, Mississauga in Fall 2010 as part of an assignment in a biometrics class taught by Helene Wagner. The poem was awarded first place in the poetry category of the 2011 CAUSE A-Mu-sing contest.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: partial/conditional tables, confounding, types of independence (mutual, joint, marginal, and conditional), identifiability constraints, partial odds ratios, hierarchical log-linear model, pairwise interaction log-linear model, conditional independence log-linear model, goodness of fit, and model building.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: conditional independence, log-linear models for 2x2 tables, expected counts, logistic regression, odds ratio, parameters of interest for different designs and the MLEs, poisson log-linear model, double dichotomy, the multinomial, and the multinomial log-linear model.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Pearson's residuals and rules for partitioning an I x J contingency tables as ways to determine association between variables.
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,