Data Presentation

  • 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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  • 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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  • 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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  • This page, part of the NIST Engineering Statistics handbook, contains links to web pages which have tables of values for various distributions.
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  • This part of the NIST Engineering Statistics handbook contains case studies for the production process characterization chapter.
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  • This chapter of the NIST Engineering Statistics handbook presents information on the statistical modeling of an engineering process. It contains an introduction, discussion of the assumptions, information about data collection and analysis, a discussion of what can be concluded from different process models, and case studies.
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  • This chapter of the NIST Engineering Statistics handbook "presents techniques for monitoring and controlling processes and signaling when corrective actions are necessary." It contains an introduction to process control, a discussion of acceptance sampling, introductions to control charts and time series modeling, tutorials for background information, and case studies.
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  • A project of the International Association of Statistics Education (IASE). After a first phase of the project led by the outstanding work of Carol Blumberg, where the emphasis was in the development of a series of webpages that will provide users throughout the world with a data bank of international statistical literacy resources for all audiences and in several languages, ISLP is now moving one step ahead. Besides continuing collecting web-based statistical literacy resources from all over the world, ISLP now actively organizes and promotes statistical literacy activities throughout the world and gets actively involved in other worlwide projects. The webpage is a forum where everyone can edit and enter their statistics literacy resources and participate in discussions.
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  • This lesson deals with the statistics of political polls and ideas like sampling, bias, graphing, and measures of location. As quoted on the site, "Upon completing this lesson, students will be able to identify and differentiate between types of political samples, as well as select and use statistical and visual representations to describe a list of data. Furthermore, students will be able to identify sources of bias in samples and find ways of reducing and eliminating sampling bias." A link to a related worksheet is included.
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  • This applets on this site include: interactive graphs of many distribution models; a collection of computer generated games; a collection of data modeling aids including curve fitting, wavelets, matrix manipulations, etc.; p-values, quantiles & tail-probabilities calculations; virtual online probability experiments and demonstrations; and a large collection of statistical techniques for online data analysis, visualization, and integration.

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