Data Collection

  • 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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  • This part of the NIST Engineering Statistics handbook contains case studies for the process improvement chapter, which deals with design of experiments.
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  • This part of the NIST Engineering Statistics handbook contains case studies for the process or product monitoring and control chapter.
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  • This chapter of the NIST Engineering Statistics handbook "presents the background and specific analysis techniques needed to compare the performance of one or more processes against known standards or one another." It contains an introduction and information about comparisons with one process, two processes, and three or more processes or samples. Topics include outliers, trends, confidence intervals for means and proportions for one sample. Also included are materials on ANOVA, Kruskal Wallis tests, tests for equivalence of variances, variance components, chi-square tests for contingency tables and multiple comparisons.
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  • This chapter of the NIST Engineering Statistics handbook "describes the terms, models and techniques used to evaluate and predict product reliability." It contains an introduction, discussions on the assumptions, and sections on reliability data collection and analysis.
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  • This reference resource explores the use of clickers, or personal response systems, in the classroom. Main points of discussion include what clickers are, who is using them, what makes them unique, why they are considered significicant, the downsides, and teaching and learning implications.
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  • This site is the Statistical Consulting Service Web Resources page for York University. It includes lists of statistical and statistical graphics resources, SAS information guides, online statistical computing applets, and a bibliography of articles for the statistics user.

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  • This site contains data sets to help teach a Chance course and help students understand issues that may not be found in a standard statistics text. Topics covered include: mean, median, random walks, regression, correlation, and more.
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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 is an exercise in interpreting data that is generated by a phenomenon that causes the data to become biased. You are presented with the end product of this series of events. The craters occur in size classes that are color-coded. After generating the series of impacts, it becomes your assigned task to figure out how many impact craters correspond to each of the size class categories.
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