Sentence examples for mean model prediction from inspiring English sources

To this end, we evaluated mean model prediction across hydrologic units (USGS 6-digit HUC units) and compared them qualitatively to the non-native occurrence data (Figure 2).

This compares to a mean model prediction of 0.556, and a mean absolute deviation between the model prediction and observation of 0.096 (i.e., model within 10% of observed for an average species).

For species with at least 30 observations of crown radius, the average crown radius had an observed mean of 2.65 m (with range of 1.40 to 4.22 m), compared to a mean model prediction of 2.68 m; and the mean deviation between the model prediction and observation was 0.118 m (i.e. predicted crown radius wrong by 12 cm for an average species).

The observed OLS slope of crown radius vs dbh had an average of 0.0692 m cm−1, with range 0.0217 to 0.2198, compared to a mean model prediction of 0.0696; for these slopes, the mean absolute deviation between model and observed was 0.0145 (i.e. 0.01 m radius per cm dbh).

The mean model prediction also appears to be quite close to many of the means reported in the literature.

At each of these exposure values, calculate the mean model prediction for all subjects within each trial/subgroup.

To address such issues, it is recommended to generate a mean exposure response model prediction by the following procedure: For each subject in the dataset, take the actual covariates (sex, body weight, etc)., and use the model to predict the response across the entire exposure range for a number of discrete prefixed exposure values.

For all three endpoints, the mean and median model predictions were within 80 to 150% of the measured means and medians, respectively.

Dynamics learning further provides us with means to model prediction uncertainty based on experienced stochastic movement data; we provide evidence that, in conjunction with an appropriate antagonistic arm and realistic motor variability model, impedance control emerges from a stochastic optimization process that minimizes these prediction uncertainties of the learned internal model.

All observations are reasonably close to the mean of model predictions and fall well within the 95% confidence interval (CI) of the values predicted by the PSA.

There is a good quantitative agreement between the mean field model predictions and numerical results obtained with VAMUCH.