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Pre-established exclusion criteria used was the Grubbs test for outliers (conducted in Prism 7).
Furthermore each data set was evaluated using the Q-test to test for outliers with some data being eliminated.
An approach frequently used is the z-value test for outliers (Songwon, 2006) in which observations are converted to z-scores in order to see which observations fall a pre-set number of standard deviations (SDs) away from the mean.
To test for outliers in both tails, the absolute residuals are calculated as (r_{ij}=|x_{ij}-{bar{x}}_j|), where (z_{ij}) denote the (x_{ij}) values sorted by their absolute residuals in ascending order.
For gene expression quantification by qPCR, the Grubb's test for outliers was calculated for each three triplicate measurement.
The outliers were detected prior to the analysis with the Grubbs' test for outliers.
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A test for outlier status of individual loci using FDIST2 [ 44] identified one locus, Cocl-Lav27, as a significant outlier (θ ST = 0.249, p < 0.0001).
Additionally, the standard deviation of the combined data pool for the three sample intakes was estimated and a Grubbs' test for outlier screening for the three sets was carried out (Table 4).
Xia et al. (2006) proposed a two-stage method, combining Dixon's Q-test and a median absolute deviation (MAD) modified z-score test, for outlier detection of peptide ratios.
Data sets were tested for outliers using Grubbs' test (extreme studentized deviate method).
To compare the power of the Healy, single-outlier Grubbs, skewness and kurtosis tests for outliers and their applicability to quality control (QC).
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