Normality Test: Data Set, Normal Distribution, Random Variable, Model Selection, Interpretations of Probability - Softcover
Synopsis
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In statistics, normality tests are used to determine whether a data set is well-modeled by a normal distribution or not, or to compute how likely an underlying random variable is to be normally distributed. More precisely, they are a form of model selection, and can be interpreted several ways, depending on one''s interpretations of probability: * In descriptive statistics terms, one measures a goodness of fit of a normal model to the data – if the fit is poor then the data is not well modeled in that respect by a normal distribution, without making a judgment on any underlying variable. * In frequentist statistics statistical hypothesis testing, one tests the data against the null hypothesis that it is normally distributed.
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In statistics, normality tests are used to determine whether a data set is well-modeled by a normal distribution or not, or to compute how likely an underlying random variable is to be normally distributed. More precisely, they are a form of model selection, and can be interpreted several ways, depending on one''s interpretations of probability: * In descriptive statistics terms, one measures a goodness of fit of a normal model to the data – if the fit is poor then the data is not well modeled in that respect by a normal distribution, without making a judgment on any underlying variable. * In frequentist statistics statistical hypothesis testing, one tests the data against the null hypothesis that it is normally distributed.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -High Quality Content by WIKIPEDIA articles! In statistics, normality tests are used to determine whether a data set is well-modeled by a normal distribution or not, or to compute how likely an underlying random variable is to be normally distributed. More precisely, they are a form of model selection, and can be interpreted several ways, depending on one's interpretations of probability: In descriptive statistics terms, one measures a goodness of fit of a normal model to the data if the fit is poor then the data is not well modeled in that respect by a normal distribution, without making a judgment on any underlying variable. In frequentist statistics statistical hypothesis testing, one tests the data against the null hypothesis that it is normally distributed. 112 pp. Englisch. Seller Inventory # 9786130496333
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