- high-resolution terminology - matching measurements at high-resolution
Outlier index - DatLab
Description
A skewness index based on average-median (SIAM) is defined for evaluation of skewness in relation to normal distribution. The SIAM is derived from Pearson’s coefficient of skewness #2:
- Pearson’s coefficient of skewness = 3 · (average-median)/SD
The skewness index SIAM introduces the absolute value of the arithmetic mean, x = ABS(average + median)/2, for normalization:
- SIAM = (average-median)/(x + SD)
- SIAM = (average-median)/[ABS(average+median)/2 + SD]
At the limit of a zero value of x, the SIAM equals Pearson’s coefficient of skewness #2 (without the multiplication factor of 3). At high x with small standard deviation (SD), the SIAM is effectively the difference between the average and the median normalized for x, (average-median)/x.
Abbreviation: SIAM, OI
Communicated by Gnaiger E (2016-10-03) updated 2021-06-07
The outlier index in DatLab
- In DatLab analysis, the skewness index SIAM is used as an outlier index OI = SIAM. 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
