Sentence examples for model root mean from inspiring English sources

The groundwater model showed higher agreement between modeled vs observed heads when spatially variable recharge forced the upper boundary of the model (root mean square error reduced from 13.5 m to 8 m).

The model root mean square error for yield prediction was highly correlated with the distance between the weather stations and the fields, for distances smaller than 10 km, and reached 0.5 t ha−1 for a 5-km distance when fine-resolution soil properties were used.

To conclude there is a good fit between the observed data and the hypothesized model, root mean square error of approximation (RMSEA) 34 should be less than 0.06 and the comparative fit index (CFI) 35 should be greater than 0.95.

Among 291 patients analyzed, the Self-Report HABC Monitor demonstrated excellent fit for the confirmatory factor analysis model (root mean square error of approximation =0.030, comparative fit index =0.974, weighted root mean square residual =0.837) and good internal consistency (0.78 0.92).

The adequacy of the model was analysed using a global index that was responsive to sample size and complexity of the model (root mean square error of approximation - RMSEA), as well as an incremental index that was less dependent on the sample size (comparative fit index - CFI).

For evaluating the ability of the models, root mean square errors, mean absolute errors and determination coefficient statistics were used.

For the MLR models, root mean square errors of calibration and validation were calculated.

Model selection was based on the differences in the two models' root mean square error of approximation (RMSEA; [ 27]) which is a measure of the empirical validity of the tested models (i.e., how well the models fit the observed data).

With step, ramp, and parabola odor inputs, the experimental outputs were closely matched by the model (root-mean-square error = 25 spike/s, Figure 3F).

RPE: Rating of perceived exertion; Wmax: Maximum work capacity; P: Power model; D: Delay model; PD: Power-delay model; QD: Quadratic-delay model; RMSE: Root mean square error.

The result showed improvement of model performance: compared with PLS model, the root mean square errors of prediction (RMSEP) of iPLS model decreased from 0.111 mg to 0.068 mg, and the R2 parameter increased from 0.9434 to 0.9801.