Sentence examples for significantly better data from inspiring English sources

Any future reconsideration of the economic argument for charging would need significantly better data on the number of requests made under the act and the costs incurred in responding to them.

The analysis, a preprint of which is published online, shows to 95% confidence that theories calling for more than six textures have it wrong - but the team concedes that significantly better data are on the way.

This indicates that the PGLS estimates provide a significantly better data fit than the OLS estimates for our sample, and therefore support the contention that the slopes we obtained are more likely to represent the true allometric slopes for the three maternal energetic investment durations than the slopes reported by Hamilton et al. [ 11].

Both SCMP and ML trees explained significantly better the data set used to reconstruct these trees than the rival data set, demonstrating the overall discordance between nuclear and chloroplast topologies.

Basically, for two phylogenetic hypotheses to be discordant, a topology A must explain significantly better a data set A than a topology B does it, and a topology B must explain significantly better a data set B than a topology A does it.

It was observed that the model prediction was significantly better for data with liquid-only Reynolds number, ReD,l > 4000.

Our results show that in 46 cases the truncated power law function fits statistically significantly better our data than the power law function (positive likelihood ratio, p-values below 0.05) while in 3 regions it is worse (negative likelihood ratio, p-values below 0.05).

Our model performs significantly better with data from the true distribution, suggesting that our predictions are due to the specific configuration of gene expression and the metabolites that we are testing and not due to a global trend in the expression data.

Using the Chi square difference test described in Eqs. 8 and 9, the SEM models with either passage position or item position effects (M3 and M4) indicated significantly better model-data fit than the Rasch model (M1), whereas the SEM model with form effects (M2) was not better than the Rasch model in terms of model-data fit.

When AICcs for individual discounting data were calculated, hyperbolic discount functions also significantly better fit individual data than exponential functions (t-tests, p s < 0.01, for all comparisons), also suggesting that subjects discounted delayed and probabilistic rewards hyperbolically.

The program fits a natural cubic spline to the time course data and then tests whether the fit to the actual data is significantly better than to the data averaged between conditions or to an invariant gene.