Resource Library

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 resource explains the t-distribution and hypothesis testing (informally) using an example on laptop quality.

This PowerPoint lecture presenation explains confidence intervals for a mean when using a small sample. It discusses the t-distribution, compares the t-statistic to the z-statistic, and provides an example of a small sample confidence interval. The original presentation is available for download.

This PowerPoint presentation dicusses general concepts of confidence intervals and interprets confidence intervals for a mean, difference in two means, and the relative risk. The original presenation is available for download.

This activity leads students to appreciate the usefulness of simulations for approximating probabilities. It also provides them with experience calculating probabilities based on geometric arguments and using the bivariate normal distribution. We have used it in courses in probability and mathematical statistics, as well as in an introductory statistics course at the post-calculus level. Students are expected to approximate the solution through simulation before solving it exactly. They are also expected to employ graphical as well as algebraic problem-solving strategies, in addition to their simulation analyses. Finally, students are asked to explain intuitively why it makes sense for the probabilities to change as they do. Key words: simulation, probability, geometry, independence, bivariate normal distribution

In this free online video, students discover an improved technique for statistical problems that involves a population mean: the t statistic for use when sigma is not known. Emphasis is on paired samples and the t confidence test and interval. The program covers the precautions associated with these robust t procedures, along with their distribution characteristics and broad applications."

In this free online video program, students will learn that "causation is only one of many possible explanations for an observed association. This program defines the concepts of common response and confounding, explains the use of two-way tables of percents to calculate marginal distribution, uses a segmented bar to show how to visually compare sets of conditional distributions, and presents a case of Simpson's Paradox. The relationship between smoking and lung cancer provides a clear example."