This site gives an explanation, a definition of and an example for tests of significance. Topics include null and alternative hypotheses for population mean, one-sided and two-sided z and t tests, levels of significance, and matched pairs analysis.
This site gives an explanation, a definition of, and an example using comparison of two means. Topics include confidence intervals and significance tests, z and t statistics, and pooled t procedures.
This site gives an explanation, a definition and an example of inference for categorical data. Topics include confidence intervals and significance tests for a single proportion, as well as comparison of two proportions.
This site gives an explanation, a definition and an example of chi-square goodness of fit test. Topics include chi-square test statistics, tests for discrete and continuous distributions.
This site provides the description and instructions for as well as the link to The Self-Avoiding Random Walk applet. In the SAW applet, random walks start on a square lattice and then are discarded as soon as they self-intersect. If a random walk survives after N steps, we compute the square of the distance from the origin, sum it up, and divide by the number of survivals. This variable is plotted on the vertical axis of the graph, which is plotted to the right of the field where random walks travel.
This section of the Engineering Statistics Handbook describes in detail the process of choosing an experimental design to obtain the results you need. The basic designs an engineer needs to know about are described in detail.
The GAISE project was funded by a Strategic Initiative Grant from ASA in 2003 to develop ASA-endorsed guidelines for assessment and instruction in statistics in the K-12 curriculum and for the introductory college statistics course.
A good resource for problems in statistics in engineering. Contains some applets, and good textual examples related to engineering. Some topics include Monte Carlo method, Central Limit Theorem, Risk, Logistic Regression, Generalized Linear .Models, and Confidence.
