Sentence examples for models explained from inspiring English sources

Both Han and Krief models explained the data pretty well.

The DGH and DG models explained more than 90% of tree AGB variation, while the D and DH models explained less than 90% of the variation.

The better models explained 10 16% of the log-likelihood of the probability of patch occupancy.

The best stepwise multiple regression using Generalized Linear Models explained 31% of the deviance for Lu.

The AMMI and GGE biplot models explained 77.49% and 75.57% of total observed genotypic variation, respectively.

GLM models explained from 31to48%8% of the total sums of squares.

Predictive models explained from 28%to67%7% of the variation in soil properties.

The models explained 97.5% variability for Cr VI) reduction efficiency and 99% variability for energy consumption.

The final RF models explained 68% (PESA), 37% (PETR) and 49% (PNCV) of the withheld variance on average.

Following optimization, both ANN and GLM models explained the data effectively (corrected c-indices 0.99 and 0.95, respectively), and both models explained the independent test data set completely (c-indices 1.00 for both models).

The CRP variables in power models explained 80.9% and 90.9% of the BA and AGB variations, respectively.