This page, part of the NIST Engineering Statistics handbook, describes the Kolmogorov-Smirnov goodness of fit test. It contains a graph of the empirical distribution function with the cumulative distribution function, a definition of the test, the questions it answers, the assumptions that it makes, and links to other goodness of fits tests and a case study.
This chapter of the NIST Engineering Statistics handbook describes how to do a production process characterization study. It contains an introduction, discussion of the assumptions, information about data collection and analysis, and case studies.
This chapter of the NIST Engineering Statistics handbook describes the measurement process characterization with discussions of control, calibration, gauge studies, and uncertainty analysis, and a set of case studies.
This site lists a set of case studies that cover regression topics, random number calculations of pi, as well as limit theorems. On the individual case study pages are the descriptions and/or instructions.
This site is an index of modules which cover probability and statistics topics including basic probability, random variables, moments, distributions, data analysis including regression, moving averages, exponential smoothing, and clustering.
