Sentence examples for multivariate model prediction from inspiring English sources

In order to increase the clinical usefulness of the findings in the multivariate model, prediction nomograms were developed in the present study (Fig.  2).

We found that peak knee flexion angle was the strongest predictor of SLS performance and that knee joint medio-lateral displacement enhanced the multivariate model prediction of SLS performance for both experienced and student raters.

By contrast, poor correlations were observed between nodes measuring signaling events in different pathways (such as Thapsigargin→p-CREB | Fold and IL-27→p-Stat5 | Fold (R = 0.04), suggesting that these nodes are measuring distinct signaling pathways and might be combined to produce a multivariate model for prediction of FLT3 receptor mutational status with higher predictive value (Figure 7).

Using least square linear regression, we fitted a multivariate model for the prediction of the number of MUs according the to number of women of all ages, of radiologists and of country surface.

When a large variation in both chemical and physical variables is defined, the traditional two-way calibration strategies, such as partial least squares (PLS), do not allow the obtaining of multivariate model with good predictive ability (nonlinear predictions and prediction error over 8% w/w).

The multivariate model provided a valid prediction of grip strength (error 2.9%) but predicted pinch strength was less close to the measured one (error 13.3%) as shown by the examples.

The development of a multivariate model for fiber content prediction in sugar cane has been investigated and can be offered as an alternative to the wet methods of analysis.

However, multivariate model can provide better prediction and might be implemented in routine for dengue surveillance in Guadeloupe.

Given probable overlap among different maltreatment experiences and trauma symptoms, the variables were included together in a multivariate model to examine unique prediction for either automatic/intrapersonal or social/interpersonal functions.

With regard to pregnancy-associated depression, stress appeared to be one of the most stable factors in multivariate models for the prediction of depression during pregnancy [ 33], making this topic interesting for further research concerning this phenotype.

This highlights the potential of multivariate model of rock mass classifications in TBM performance prediction.