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Tutorial

  • This resource defines and explains Chi square. It takes the user through 5 different categories: 1) Testing differences between p and pi 2) More than two categories 3) Chi-square test of independence 4) Reporting results 5) Exercises.

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  • This one-page document gives advice on how to construct and give exams. It focuses on making exams a positive experience for both instructors and students. It is written by Rich Felder an expert in Engineering education.
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  • This site discusses survey questionnaires and interviews, provides links to detailed descriptions and pros and cons of each, and describes how to conduct them.
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  • This site explains the 2x2 factorial experimental design, it's components, and it's effects. Graphs illustrate the concepts discussed.
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  • This particular textbook lesson addresses the independent t-test. It presents to the user how to compute the t statistic and then how to interpret the results.
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  • Textbook-like example showing the independent t-test. Gives a nice way for students to think through the problem and interpret results.
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  • An explanation of scatter plots, their use, purpose and interpretation. It provides examples of the various relationships described by scatter plots as well as case studies and related techniques.
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  • This tutorial on the Two Sample t test includes its definition, assumptions, hypotheses, and results as well as tests for equal variance and graphical comparisons. An example using output from the WINKS software is given, but those without the software can still use the tutorial. An exercise is given at the end that can be done with any statistical software package.
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  • This tutorial illustrates the basic principles of the Central Limit Theorem and enhances conceptual understand of why the Central Limit Theorem is important to inferential statistics.
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  • This section of the Engineering Statistics Handbook gives the normal probability density function as well as the standard normal distribution equations. Example graphs of the distributions are shown and a justification of the Central Limit Theorem is included.
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