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Crash Course on Basic Statistics

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Crash Course on Basic StatisticsMarina Wahl, of New York at Stony BrookNovember 6, 20132Contents1 Basic Basic Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Probability of Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayes Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .62 Basic Types of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Errors.

Content validity: how well the process of mea-surement re ects the important content of the domain of interests.? Concurrent validity: how well inferences drawn from a measurement can be used to pre-dict some other behaviour that is measured at approximately same time.? Predictive validity: the ability to draw infer-ences about some event in ...

  Measurement, Statistics, Measuremen t, Surement

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