Resource Library

This course features a full set of lecture notes and problem sets introducing students to the modeling, quantification, and analysis of uncertainty. Topics covered include: formulation and solution in sample space, random variables, transform techniques, simple random processes and their probability distributions, Markov processes, limit theorems, and elements of statistical inference.

This course in Statistical Mechanics features problem sets and exams. Basic principles examined include: the laws of thermodynamics and the concepts of temperature, work, heat, and entropy; postulates of classical statistical mechanics, microcanonical, canonical, and grand canonical distributions; applications to lattice vibrations, ideal gas, photon gas; quantum statistical mechanics; Fermi and Bose systems; and interacting systems: cluster expansions, van der Waal's gas, and mean-field theory.

This course is second in the series of undergraduate Statistical Physics courses and features comprehensive lecture notes and assignments. Course topics include probability distributions for classical and quantum systems; microcanonical, canonical, and grand canonical partition-functions and associated thermodynamic potentials; conditions of thermodynamic equilibrium for homogeneous and heterogeneous systems; non-interacting Bose and Fermi gases; mean field theories for real gases, binary mixtures, magnetic systems, polymer solutions; phase and reaction equilibria, critical phenomena; fluctuations, correlation functions and susceptibilities, and Kubo formulae; evolution of distribution functions: Boltzmann and Smoluchowski equations.

This article contains practical information on teaching statistics to a political science class.

This page contains information and links about statistical literacy. Some links are to textbooks, online articles, resources, and information about upcoming events.

This page discusses disadvantages of large datasets with regard to Simpson's Paradox.

The applets in this section demonstrate basic issues of experimental design. The Poor Experimental Design ignores randomization rules and allows for increased experimental error. The Improved Experimental Design offers improvement over the first design by adding randomization and reducing experimental error. Both applets require the input of several participants. The purpose of the applets is to test the reaction times between a participant's dominant and non-dominant hand. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/ExpDesign.html

This interactive module helps students to understand the definition of and uses for clustering algorithms. Students will learn to categorize the types of clustering algorithms, to use the minimal spanning tree and the k-means clustering algorithm, and to solve exercise problems using clustering algorithms.

This free online video program "presents a detailed case study of statistics at work. Operating in a real-world setting, the program traces the practice of statistics - planning the data collection, collecting and picturing the data, drawing inferences from the data, and deciding how confident we can be about our conclusions. Students will begin to see the full range and power of the concepts and techniques they have learned." This individual video is accessed by scrolling down to the "Individual Program Descriptions - 26. Case Study " and click the "VOD" icon at the top-right of the description.

This demonstration allows you to view the binomial distribution and the normal approximation to it as a function of the probability of a success on a given trial and the number of trials. It can be used to compute binomial probabilities and normal approximations of those probabilities.