Sentence examples for the random average from inspiring English sources

As shown in Table 2, a value of 1.04 is more than three standard deviations away from the random average of 0.59 for ComboT CT-opt.

Truchon and Bayly [9] have analytically calculated the random average and obtained an exact formula, RIE = N n ∑ i = 1 n e − α r i / N 1 − e − α e α / N − 1. (4).

These statistics suggest that the 2-D and 3-D neighboring thresholds are 3.7 and 4.1 standard deviations away from the random average values, respectively [i.e., 3.7 = (0.90 − 0.42)/0.13 for 2-D similarity and 4.1 = (1.3 − 0.77)/0.13 for 3-D similarity].

The random average under parallel analysis criterion suggested that two components should be retained in both samples, which represented just under 50% of the variation in the original data in the upper Eocene but around 64% in the middle Eocene (Table 1).

If our findings would only reflect the random averaging of thousands of neurons within each vertex, we would not expect to observe this dependence of ERRF on sound level.

The frequency of having random average r values greater or equal to the observed average r value was taken as the P value of observing the level of co-expression in the gene cluster with n genes.

Most of the networks had short path lengths (all average λs ≤ 1.05 and all SDs ≤ 0.06) and larger clustering coefficients than the random networks (average γs reaching values of up to 1.48 with all SDs ≤ 0.33).

All structures are interrelated at a ComboT of 1.04, more than 3.2 standard deviations beyond the random pair average of 0.59.

With that said, at ten diverse conformers per compound, there still seems to be additional room for further increases in the random distribution average if one was to consider using more diverse conformers per compound.

Figure 3B shows that, for all urea concentrations, Rh of Nank4−7* is below the random coil average and thermally denatured protein is on the border of the 95% confidence line.

The difference of the model accuracy before and after the random permutations, averaged over all trees in the forest, tells us how important that predictor is for determining the outcome.