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A Technique to Rescue Non-Parametric Outlier Data Using SAS®

For many scientists and data analysts, outliers are like a ‘black box’ in conventional statistics. Many believe that these outlier observations arise due to errors or due to improper procedures in the experiment. Majority of them eliminate the outliers unscientifically by brute force. Some identify them statistically but discard them as if they are junk. Some understand importance of the outliers but they do not know how to deal with it. If you are one among them or interested in scope of the outliers, then this paper is the right resource for you. Outliers are like hidden treasure in data analytics. Discarding true outliers from data may costs huge amount of money in certain projects such as clinical trials. An innovation with true outliers in data analytics using SAS is shown in the figure below: 

For more details refer following links:

Refer My Paper Presented At The PhilaSUG Fall Meeting 2015

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Refer My Presentation At The PhilaSUG Fall Meeting 2015 (SlidesShare)

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Refer following website:

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Tags: Analytics, Data, Kruskal-Wallis, Non-parametric, Outliers, SAS, Wilcoxon, linear, macro, modelling, More…programming, scores, test

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