Resource Library

Song about the need to show a significant result in order to have a manuscript published. May be sung to the tune of Robert Feldman, Gerald Goldstein and Richard Gottehrer's 1963 song "My Boyfriend's Back," popularized by The Angels. Lyrics by Marc Coram and Matthew Finkelman (December, 2003). This song is part of the "Stanford Statistics Songbook" found at www.bscb.cornell.edu/~hooker/StanfordStatisticsSongbook.pdf Free to use for non-commercial educational purposes. Contact author to use in publications or for commercial purposes. Musical accompaniment realization and vocals are by Joshua Lintz from University of Texas at El Paso.

Song about the pleasure of teaching statistics when the class is engaged. May be sung to the tune of John Lennon and Paul McCartney's 1963 song "I Want to Hold Your Hand." Lyrics by Armin Schwartzman (December, 2003). This song is part of the "Stanford Statistics Songbook" found at www.bscb.cornell.edu/~hooker/StanfordStatisticsSongbook.pdf Free to use for non-commercial educational purposes. Contact author to use in publications or for commercial purposes. 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: 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: 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: iterative solutions to non-linear equations, score equations for exponential class variables, Newton-Raphson vs. Fisher’s Scoring, Logistic Regression for an R × 2 tables, saturated model, odds ratios when rows are not ordinal, goodness of fit, likelihood ratio statistic for nested models, and residuals.

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: multinomial distribution, LaGrange multipliers, Exact Multinomial Test (EMT), the Pearson statistic, and goodness of fit.

There are more than a dozen different fit statistics researchers use to assess their confirmatory factor analyses and structural equation models. Here we have assembled a list of the most popular fit statistics used and recommended cut-offs that indicate a good fit. 

Random is a website devoted to probability, mathematical statistics, and stochastic processes, and is intended for teachers and students of these subjects. The site consists of an integrated set of components that includes expository text, interactive web apps, data sets, biographical sketches, and an object library. 

A "12 page" tutorial that explores the liner models via excel spreadsheets. The learning module leads the user through various aspects of linear modeling. This tutorial includes a worksheet that allows students to vary the scatter (or noise) level, by adjusting the scroll bar or by clicking on the arrows, to see how the slope and intercept of line respond to the addition of scatter to the data, while monitoring the value of r^2.

This resource defines and explains Chi square. It takes the user through 5 different categories: 1) Testing differences between p and pi 2) More than two categories 3) Chi-square test of independence 4) Reporting results 5) Exercises.