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Compared with Equations 50 and 51, we can see that the FDA beampattern variances at both kinds of random frequency increment error distributions are dependent on the statistics properties.
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Yet, an AMOVA with groups defined a priori by the four connectivity regions of Cowen et al. (2006) shows a small and insignificant proportion of the variance at both loci partitioned among these regions (Table 2).
For the parent-reported YSI scores, this single factor explained 79% of the variance at both time points.
Its performance was almost identical to PEVNF2, PEVAF3, and PEVGC3, which had low sampling variance at both high and low PEV.
In models 3 and 4, the addition of the neighbourhood- and individual-level parameters substantially reduced the random effects variance at both LSOA and unitary authority levels.
The number of breaths required to achieve 95%% of the additional volume according to each level of PEEP was tested using analysis of variance at both increase and decrease in PEEP.
We then compared the amount of variance explained among the three models, and assessed whether the variance at both the unit and nursing home levels was greater than 0. We used the Likelihood Ratio test to assess differences among the three models.
To account for intra-cluster correlation, and to enable the modelling of variance at both area level and individual level, we performed a multi-level analysis of unweighted data, with the middle layer super output area specified as the grouping variable and individuals nested within these areas.
Notice, for example, the relatively lower variances at degrees (n approx 10-40) in both cases.
The effects of the fuel properties were compared by using a SS-ANOVA (smoothing spline analysis of variance) method at both a low and a high rail pressure.
The models were used to determine variance estimates at both the elephant and camp level.
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CEO of Professional Science Editing for Scientists @ prosciediting.com