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JMP 12 Basic Analysis by SAS Institute

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Explore Outliers Utility
Exploring and understanding outliers in your data is an important part of analysis. Outliers in data can be due to mistakes in data collection or reporting, measurement systems failure, or the inclusion of error or missing value codes in the data set. The presence of outliers can distort estimates. Therefore, any analyses that are conducted are biased toward those outliers. Outliers also inflate the sample variance. Sometimes retaining outliers in data is necessary, however, and removing them could underestimate the sample variance and bias the data in the opposite direction.
Whether you remove or retain outliers, you must locate them. There are many ways to visually inspect for outliers. For example, box plots, histograms, ...

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