Sentence examples for less variance and from inspiring English sources

When there is less variance, and all else being equal, the covariance, and thus the correlation, is attenuated or reduced.

While PM is more sensitive to the increase in accepted actives ((frac{P}{N}) decreases the influence of false positives for PM, see Eq. 2), Pre value shows less variance and it quickly approaches zero because the list of the top hits gets "flooded" with false positives.

In the present study, the 20-item ITAS total score explained less variance and displays lower internal consistency reliability than the 16-item 'negative appraisal' score.

Phylogenetic trees derived from models in the 95% confidence interval set showed much less variance and were significantly closer to the tree estimated under the best-fit model than trees derived from models outside this interval.

However, using REML it is not feasible to utilize mixture priors to better discriminate between SNPs which contribute more or less variance, and to partition the variances at the sub-chromosome level, which is all straightforward in a Bayesian approach.

In contrast, the AUDIT-C, which only covers one domain, explained considerably less variance and had lower correlation to the AUDIT-10 than novel combinations, which was also observed in a previous computerised survey of young people [ 37].

As can be seen in Fig. 3, there is less variance between social and environmental innovations in this comparison than in the previous two comparisons (Fig. 4).

Whole watershed land use explained slightly less variance, but riparian and whole watershed land use were so tightly correlated that it was difficult to separate their effects.

Assuming that an active quality management policy at the care group level will focus on practices that perform less well, it is plausible that a higher level of quality management on care group level would result in less variance between practices and less 'bad' practices.

The application of geometrical parameters to TFR diagrams provides an innovative way to complement classical techniques based on one dimensional US and an alternative way of obtaining similar meaning and less variance estimators (as we will show in Section 5).

This technique also provides an alternative method of obtaining similar meaning and less variance estimators.