Validating dmi pool

29 Feb

In statistics, normality tests are used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random variable underlying the data set to be normally distributed.More precisely, the tests are a form of model selection, and can be interpreted several ways, depending on one's interpretations of probability: An informal approach to testing normality is to compare a histogram of the sample data to a normal probability curve.

validating dmi pool-25validating dmi pool-66validating dmi pool-52

Lack of fit to the regression line suggests a departure from normality.(see Anderson Darling coefficient and minitab) A graphical tool for assessing normality is the normal probability plot, a quantile-quantile plot (QQ plot) of the standardized data against the standard normal distribution.

Here the correlation between the sample data and normal quantiles (a measure of the goodness of fit) measures how well the data are modeled by a normal distribution.

For normal data the points plotted in the QQ plot should fall approximately on a straight line, indicating high positive correlation.

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