Joke from "The Little Black Book of Business Statistics", by Michael C. Thomsett (1990, Amacom) p. 117. also quoted in "Statistically Speaking" compiled by Carl Gaither and Alma Cavazos-Gaither.
During this simulation activity, students generate sampling distributions of the sample mean for n = 5 and n = 50 with Fathom 2 and use these distributions to confirm the Central Limit Theorem. Students sample from a large population of randomly selected pennies. Given that the variable of interest is the age of the pennies, which has a geometric distribution, this is a particularly convincing demonstration of the Central Limit Theorem in action. This activity includes detailed instructions on how to use Fathom to generate sampling distributions. The author will provide the Fathom data file upon request.
This JAVA applet assists the user in developing skills to classify a problem as one of the various types of confidence intervals, hypethesis tests and Chi Squared tests. This is not an easy application, but the comprehensive hints provided will improve the users skills in making such classifications.
This website provides links to instructions for performing basic statistics such as confidence intervals, hypothesis tests, discrete distributions, linear regression, etc. for TI 83, TI 84, and TI 86 calculators.
This is a Java Applet, which allows you to load your personal data and edit data. It provides interval and endpoint values and the option to view as a histogram.
This lesson is based on Lawrence Lesser's article that describes the set-up of the spreadsheet simulation and Cindia Stewart's lesson that seeks to answer the Birthday Problem using three different methods. Probability topics include: Sample Size, Law of Large Numbers, Complementary Probabilities, Independence of Events
This probability activity discusses the differences among various kinds of studies and which types of inferences can legitimately be drawn from each, as well as how sample statistics reflect the values of population parameters and use sampling distributions as the basis for informal inference. The procedure and assessment are provided.
