Sentence examples for more variance on from inspiring English sources

ACC explains more variance on effort than delay trials (positive-going values), whereas the converse is true for OFC (negative-going values).

For example, as 72%to92%2% of the responses fell within the first two response categories, the SPCS scale may be strengthened by expanding the response scale to capture more variance on the social relationship and school connectedness items.

As the first attempt at a continental scale, our study shows that climatic and geographic variables contributed much more to trait variations than soils did, of which the minimum temperature was most critical, followed by longitude, and soil properties explained more variances on crude protein content than on others.

Lidar-derived structural variables provided no improvement to prediction of the first NMDS axis estimates, and a minimal improvement (3% more variance explained) on NMDS2.

By subtracting one trial type from the other, this revealed that ACC explained more variance in DLPFC firing on effort trials than delay trials, whereas the converse was true for OFC".

Both areas were found to influence DLPFC activity, but strikingly, ACC explained more variance in DLPFC firing on effort trials than delay trials, whereas the converse was true for OFC.

DOI: http://dx.doi.org/10.7554/eLife.11945.026 We now note these two null results in the main text: "Both areas were found to influence DLPFC activity, but strikingly, ACC explained more variance in DLPFC firing on effort trials than delay trials, whereas the converse was true for OFC.

Their subtitle frequency measure, based on a corpus of 40 million words, explained nearly 10% more variance in lexical decision times (based on 14,000 monosyllabic and disyllabic words) than the existing golden standard, the Celex frequencies [17], [18].

The first scheme results in more accurate RTTs, because in the second scheme overhead and its variation, when data are really accessed on disks, raises more variance in observed RTTs.

Nagelkerke's R2 is functionally similar to the R2 value in linear models, ranging between 0 and 1 with higher values explaining more variance, with this variant being calculated on the log-likelihood scale.

When an eigenvalue generated from the exploratory factor analysis is higher than the random eigenvalue generated from the parallel analysis, it can be assumed that the eigenvalue represents a real factor, that accounts for more variance than a parallel component based on random data (see Hayton et al. 2004).