Data Collection

  • This site is a glossary of statisical terms searchable by topic or in alphabetical order. Topics include: Basic Definitions, Presenting Data, Sampling, Probability, Confidence Intervals, Hypothesis Testing, Paired Data, Correlation and Regression, Design of Experiments and ANOVA, Categorical Data, Non-parametric Methods, and Time Series Data.
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  • This section in the Engineering Statistics Handbook takes a data set and walks the user through analysis and experimental design based on the data.
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  • A collection of links to video workshops for students in mathematics. Includes many topics from statistics to math and science to algebra.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • Discusses Markov chains, transition probabilities, and the transition probability matrix.
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  • Discusses the benefits of Taguchi methods applied to engineering.
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  • The user is be able to change the mean and the standard deviation using the sliders and see the density change graphically. The check buttons (68, 95, 99) will help one realize the appropriate percentages of the area under the curve. An example of thiis "68-95-99.7" rule follows.
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