Functions > Data Analysis > Outliers and NaN > About Outliers and NaN Functions
About Outliers and NaN Functions
Outlier Detection and Removal
Outliers are data points that seem not to be genuine because they lie far outside of the expected behavior of the data. In general, you may want to remove outliers before fitting data because they can throw off statistics and fitted curves.
• Grubbs, GrubbsClassic, ThreeSigma, trim—Detection of outliers using the Grubbs or the ThreeSigma method and removal of outliers from data sets.
NaN Detection and Removal
• IsNaN, markNaN, matchNaN, filterNaN—Identification of data as Not a Number (NaN) and removal of NaN from data sets.
About Data Analysis Functions
About the NaN (Not a Number) Built-In Constant
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