Resource Library

This part of the NIST Engineering Statistics handbook contains case studies for the measurement process chapter.

This chapter of the NIST Engineering Statistics handbook describes Exploratory Data Analysis with an introduction, a discussion of the assumptions, a description of the techniques used, and a set of case studies.

This set of pages is an introduction to Maximum Likelihood Estimation. It discusses the likelihood and log-likelihood functions and the process of optimizing.

This journal article is a summary of resampling methods such as the jackknife, bootstrap, and permutation tests. It summarizes the tests, describes various software to perform the tests, and has a list of references.

This set of pages describes software the author wrote to implement bootstrap and resampling procedures. It also contains an introduction to resampling and the bootstrap; and examples applying these procedures to the mean, the median, correlation between two groups, and analysis of variance.

This online, interactive lesson on point estimation provides examples, exercises, and applets concerning estimators, method of moments, maximum likelihood, Bayes estimators, best unbiased estimators, and sufficient, complete and ancillary statistics.

This site contains lessons which include steps, examples, and a calculator, on standard deviation, Pearson's r, t-test, one-way ANOVA, and Tukey's Post Hoc Test.

This lesson deals with the statistics of political polls and ideas like sampling, bias, graphing, and measures of location. As quoted on the site, "Upon completing this lesson, students will be able to identify and differentiate between types of political samples, as well as select and use statistical and visual representations to describe a list of data. Furthermore, students will be able to identify sources of bias in samples and find ways of reducing and eliminating sampling bias." A link to a related worksheet is included.

This collection of applets simulate many different statistical concepts such as: distributions, correlation, hypothesis testing, regression, and ANOVA.

This online, interactive lesson on special distributions provides examples, exercises, and applets covering normal, gamma, chi-square, student t, F, bivariate normal, multivariate normal, beta, weibull, zeta, pareto, logistic, lognormal, and extreme value distributions.