Difference between revisions of "Outlier index - DatLab"
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{{MitoPedia | {{MitoPedia | ||
|abbr=''OI'' | |abbr=''OI'' | ||
|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] | |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] #2: | ||
: | : Pearson’s coefficient of skewness = 3 · (average-median)/SD | ||
The ''OI'' introduces the absolute value of the arithmetic mean, ''x'' = ABS(average + median)/2, for normalization: | |||
: ''OI'' = (average-median)/(''x'' + SD) | |||
: | : ''OI'' = (average-median)/[ABS(average+median)/2 + SD] | ||
At the limit of a zero value of ''x'', 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''. | |||
}} | }} | ||
Communicated by [[Gnaiger | Communicated by [[Gnaiger E]] (2016-10-03) updated 2021-06-07 | ||
== The outlier index in DatLab == | |||
:::: In DatLab analysis, the ''OI'' is more specific than Pearson’s coefficient of skewness 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. | |||
== Outlier == | |||
::::» [[Outlier]] | |||
::::» [http://www.statisticshowto.com/pearsons-coefficient-of-skewness/ Pearson’s coefficient of skewness], [https://ww2.amstat.org/publications/jse/v19n2/doane.pdf Doane_2011_J Statistics Education: Measuring skewness: a forgotten statistic?] | |||
{{MitoPedia O2k and high-resolution respirometry | {{MitoPedia O2k and high-resolution respirometry | ||
|mitopedia O2k and high-resolution respirometry=DatLab, Oroboros QM | |mitopedia O2k and high-resolution respirometry=DatLab, Oroboros QM | ||
}} | }} | ||
Revision as of 07:15, 7 June 2021
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 #2:
- Pearson’s coefficient of skewness = 3 · (average-median)/SD
The OI introduces the absolute value of the arithmetic mean, x = ABS(average + median)/2, for normalization:
- OI = (average-median)/(x + SD)
- OI = (average-median)/[ABS(average+median)/2 + SD]
At the limit of a zero value of x, 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.
Abbreviation: OI
Communicated by Gnaiger E (2016-10-03) updated 2021-06-07
The outlier index in DatLab
- In DatLab analysis, the OI is more specific than Pearson’s coefficient of skewness 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.
Outlier
MitoPedia O2k and high-resolution respirometry:
DatLab,
Oroboros QM