Difference between revisions of "Outlier index - DatLab"
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''OI'' = (median-average)/(''Y'' + SD) | ''OI'' = (median-average)/(''Y'' + SD) | ||
The threshold of the absolute value of the ''OI'' is set at 0.05. If ABS(''OI'')>0.05 calculated for the data points within a defined [[Marks in | The threshold of the absolute value of the ''OI'' is set at 0.05. If ABS(''OI'')>0.05 calculated for the data points within a defined [[Marks in DatLab |mark]], the Mark window shows indicates the likely occurrence of outliers in the data sequence. | ||
}} | }} | ||
{{MitoPedia concepts | {{MitoPedia concepts |
Revision as of 11:57, 3 October 2016
Description
An outlier index (OI) is defined for DatLab analysis, derived from Pearson’s coefficient of skewness, but more specific in targeting outliers in a data series recorded with the O2k. At the limit of a zero value, Y = ABS(median + average)/2, the OI equals Pearson’s coefficient of skewness #2 (without the multiplication factor of 3). At high Y with small standard deviation (SD), the outlier index is effectively the difference between the average and the median normalized for the absolute value, (median-average)/Y. The definition of the outlier index is,
OI = (median-average)/(Y + SD)
The threshold of the absolute value of the OI is set at 0.05. If ABS(OI)>0.05 calculated for the data points within a defined mark, the Mark window shows indicates the likely occurrence of outliers in the data sequence.
Abbreviation: OI
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