The data were less well fit by a model that scaled the strength of responses.
Those with externally generated data were less likely to have a medium or large effect (20% vs. 34.8% self-generated data).
Differences between optimum values of dependent variables obtained by normalized design and experimental data were less than 20%.
Haemodynamic duplex data were less important than B-mode imaging in discriminating between normal and abnormal reconstruction.
On the basis of theory analysis, a correlation of two-phase natural circulation was obtained, and relative errors of 95% test data were less than ±16%.
Moreover, due to the denser and more complex vegetation cover, SMOS data were less correlated with the in situ data than for the Kenaston agricultural site.
The results indicated that the differences of testing data were less than 1.5%, compared with the theoretical values by the method of selective dissolution combined with thermal analysis.
Their data were less clear on the relationships between nominate vilissimus and Zimmerius parvus (Mistletoe Tyrannulet), sampling just one individual of each taxon and finding that they are in a clade with Zimmerius albigularis (Choco Tyrannulet), and may or may not be sister taxa.
Their data were less clear on the relationships between parvus and nominate vilissimus (Guatemalan Tyrannulet), sampling just one individual of each taxon and finding that they are in a clade with Zimmerius albigularis (Choco Tyrannulet), and may or may not be sister taxa.
After ANN training, correlation coefficients (R2) of both engine performance values for testing and training data were very close to 1. Similarly, root-mean-square error (RMSE) and mean error percentage (MEP) values for the testing and training data were less than 0.02% and 3.5% respectively.
This supported the fact that the data were less variable, more uniform, and homogeneous.
