Difference between revisions of "Outlier index - DatLab"
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|abbr=''OI'' | |abbr=''OI'' | ||
|description=An '''outlier index''' (''OI'') is defined for | |description=An '''outlier index''' (''OI'') is defined for evaluation of skewness in relation to normal distribution. The ''OI'' is derived from [http://www.statisticshowto.com/pearsons-coefficient-of-skewness/ Pearson’s coefficient of skewness], introducing the absolute value of (Average+Median)/2 for normalization. At the limit of a zero value of ''x'' = ABS(Average + Median)/2, the ''OI'' equals Pearson’s coefficient of skewness #2 (without the multiplication factor of 3). At high ''x'' with small standard deviation (SD), the ''OI'' is effectively the difference between the Average and the Median normalized for ''x'', (Average-Median)/''x''. The definition of the outlier index is, | ||
: ''OI'' = (Average-Median)/('' | : ''OI'' = (Average-Median)/(''x'' + SD) | ||
: ''OI'' = (Average-Median)/[ABS(Average+Median)/2 + SD] | : ''OI'' = (Average-Median)/[ABS(Average+Median)/2 + SD] | ||
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: Pearson’s coefficient of skewness #2 = 3 x (Average-Median)/SD | : Pearson’s coefficient of skewness #2 = 3 x (Average-Median)/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 - DatLab |Mark]], the Mark window indicates the likely occurrence of outliers in the data sequence. The threshold can be set to a lab-specific or session-specific value different from the default value. | In DatLab analysis, the ''OI'' is more specific for targeting outliers in data series recorded with the O2k. 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 - DatLab |Mark]], the Mark window indicates the likely occurrence of outliers in the data sequence. The threshold can be set to a lab-specific or session-specific value different from the default value. | ||
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Communicated by [[Gnaiger Erich]] () updated 2021-06-07 | |||
{{MitoPedia concepts}} | {{MitoPedia concepts}} | ||
{{MitoPedia methods}} | {{MitoPedia methods}} | ||
Revision as of 06:41, 7 June 2021
- high-resolution terminology - matching measurements at high-resolution
Outlier index - DatLab
Description
An outlier index (OI) is defined for evaluation of skewness in relation to normal distribution. The OI is derived from Pearson’s coefficient of skewness, introducing the absolute value of (Average+Median)/2 for normalization. At the limit of a zero value of x = ABS(Average + Median)/2, the OI equals Pearson’s coefficient of skewness #2 (without the multiplication factor of 3). At high x with small standard deviation (SD), the OI is effectively the difference between the Average and the Median normalized for x, (Average-Median)/x. The definition of the outlier index is,
- OI = (Average-Median)/(x + SD)
- OI = (Average-Median)/[ABS(Average+Median)/2 + SD]
By comparison,
- Pearson’s coefficient of skewness #2 = 3 x (Average-Median)/SD
In DatLab analysis, the OI is more specific for targeting outliers in data series recorded with the O2k. 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 indicates the likely occurrence of outliers in the data sequence. The threshold can be set to a lab-specific or session-specific value different from the default value.
Abbreviation: OI
Communicated by Gnaiger Erich () updated 2021-06-07
MitoPedia O2k and high-resolution respirometry: DatLab, Oroboros QM
Communicated by Gnaiger E 2016-10-03; updated 2016-10-22.
