This site gives an explanation, a definition of and an example using experimental design. Topics include experimentation, control, randomization, and replication.
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 lesson on observational studies discusses the nature of such studies, the relationships between various data sets, and regression. Graphs illustrate the relationships, and exercises at the end test the user's comprehension and understanding. It is taken from the online textbook for West. Mich. Univ. online introductory stats course.
The function of this site is to collect, compile, analyse, abstract and publish statistical information relating to the commercial, industrial, financial, social, economic and general activities and condition of the people.
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.
This site briefly defines several different types of sampling methods, contrasts probability and nonprobability sampling, and discusses target population. Part of a tutorial on questionnaire and survey design.
This Java based applet gives students an opportunity to work through confidence interval problems for the mean. The material provides written word problems in which an individual must be able to correctly identify the given parts for a confidence interval calculation, and then be able to use this information to find the confidence interval. It gives step by step prompts to encourage students to choose the correct numbers and "cast of characters".
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.
