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Central Limit Theorem 50
Laws of Large Numbers 34
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  • **Normal Approximation to the Binomial Distribution

    This demonstration allows you to view the binomial distribution and the normal approximation to it as a function of the probability of a success on a given trial and the number of trials. It can be used to compute binomial probabilities and normal approximations of those probabilities.

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  • Video: Against All Odds: 23. Control Charts

    In this free online video program, "the successes of casino owners and the manufacturing industry are used to demonstrate the use of the central limit theorem. One example shows how control charts allow us to effectively monitor random variation in business and industry. Students will learn how to create x-bar charts and the definitions of control limits and out-of-control limits."

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  • Stattucino Statistical Analysis

    Stattucino is a free Java-based system for data analysis. This service is available as a Java applet or application. Some statistics are provided by a web-based interface as servlets. The applet and the application have a spreadsheet type interface for entering data, whereas the servlets use a html form for entering data. The output produced by the servlets, the applet and the application are in html.

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  • Sampling SIM program: Confidence Intervals

    This webpage provides instructions for teaching confidence intervals using Sampling SIM software. It includes information regarding prerequisite knowledge, common misconceptions, and objectives, as well as links to an activity and a pre/post-test.

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  • Sampling SIM Program: P-values and Standard Distributions

    This webpage provides instructions for teaching p-values and standard distributions using Sampling SIM software. It includes information regarding prerequisite knowledge, common misconceptions, and objectives, as well as links to an activity and a pre/post-test.

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  • Process Improvement (Engineering Statistics Handbook)

    This chapter of the NIST Engineering Statistics handbook provides information on the proper design of experiments. It contains an introduction, a discussion of assumptions, a description of different design types, a discussion of the analysis of data, and case studies.

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  • Gallery of Distributions (Engineering Statistics Handbook)

    This page, part of the NIST Engineering Statistics handbook, contains links to web pages describing most of the more commonly used distributions.

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  • Kolmogorov-Smirnov Goodness-of-Fit Test (Engineering Statistics Handbook)

    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.

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  • Production Process Characterization (Engineering Statistics Handbook)

    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.

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  • Measurement Process Characterization Case Studies (Engineering Statistics Handbook)

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

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