Resource Library

A cartoon that can be helpful in introducing scree plots and their interpretation in an exploratory principal components analysis to determine the number of factors to be used.  The cartoon is arendition of Edvard Munch’s famous painting “The Scream”. The cartoon was used in the January 2019 CAUSE cartoon caption contest and the winning caption was submitted by Larry Lesser from The University of Texas at El Paso. An alternative caption: "The feeling you get when the p-value is 0.055," was submitted by Minu Bhunia, a student at University of Minnesota, and can be used in discussing the interpretation of p-values and the arbitrary nature of the 0.05 cutoff.The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.

Song about the use of the logarithmic transformation in statistics. May be sung to the tune of "Hound Dog" which was popularized by Elvis Presley. Lyrics written by Dennis Pearl with assistance from Deb Rumsey. Musical accompaniment realization and vocals are by Joshua Lintz from University of Texas at El Paso.

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: Mantel-Haenszel estimator of common odds ratio, confounding in logistic regression, univariate/multivariate analysis, bias vs. variance, and simulations.

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: absolute/relative measures, number needed to treat (NNT), relative risk, odds ratio, the delta method (with a multivariate extension), and a variance covariance matrix. 

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.