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By evaluating the deviations between experimental and correlated data, all the correlated data by six models used in this research show comparable precision to those attained by careful experimental measurement.
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We validated precision by evaluating the relative deviation of triplicate measurements at each sampling location.
The accuracy of this reparameterization was assessed by evaluating the maximum absolute deviation between the old [ 2] and new growth response curves.
The score for an interaction is calculated by evaluating the following equation for three descriptors: the deviation Δ from the ideal distance and the deviation from the ideal bond angles for each of the interaction partners.
The final standard error was estimated by evaluating the 2-ΔΔCt term using ΔΔCt plus standard deviation and ΔΔCt minus the standard deviation [ 33].
The range of variation was determined by evaluating the expression: {2̂-[(CtcPh-Ct18S ± standard deviation]}10̂7, where the standard deviation is calculated as √ σcPh+σ18S).
The first method, the distributional approach, imparts meaning to the HRQL score by evaluating the changes in the HRQL scores and their associated variability (i.e., standard deviation).
Start by evaluating the relationship.
The Ewens-Watterson test statistic identifies positively selected marker loci by evaluating significant deviation from expected heterozygosity (Dh/sd) at individual loci in a single population [24], [25].
(1) The Ewens-Watterson (EW) test statistic which identifies positively selected loci by evaluating significant deviation from expected heterozygosity (Dh/sd) in a single population [ 76] was calculated using BOTTLENECK v.1.2.02 [ 38].
The operability is evaluated by the deviation ratio.
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