Sentence examples for mean bias difference from inspiring English sources

When tested against hourly measurements, the model exhibits a coefficient of variance (R2) equal to or better than 0.96, and root mean square difference (RMSD) in the range of 7.3 7.9% and mean bias difference (MBD) of −4.5% to 3.5%.

The comparison between simulated and experimentally measured outlet air temperatures showed a good agreement: a root mean square error on the outlet air temperature of about 0.50 K and a mean bias difference of 0.15 K were observed for experiments conducted on a bright sunny day.

When tested with an independent data set, the multiple regression model performed best with a higher coefficient of variance R2 (0.78 vs. 0.70), lower root mean square difference (RMSD) (12.92% vs. 13.05%) and the same mean bias difference (MBD) of −2.20%.

Figure  1 illustrates the mean bias (difference) between the SWA and indirect calorimetry as a continuous time function with 95% confidence intervals.

In their simulation study, Robitzsch and Rupp [ 19] observed that when the missing data rate was 30% and data were missing at random, mean bias (difference between true and observed differences in item difficulty between groups) was 0.60, nearly two standard deviations above average bias across all conditions.

The mean bias (average difference between catheter readings and CO-oximeter values), precision (standard deviation of the bias) and limits of agreement (bias ± 2 SD of bias) were -0.17, 4.7 and -0.17 ± 9.4 respectively.

Agreement between mGFR and eGFR was assessed using the mean bias (average difference between mGFR and eGFR), standard error of the mean bias, the Pearson correlation coefficient and the concordance correlation coefficient as described previously [ 25– 27].

The decision-tree approach for national upscaling provides a better result, with mean biases (net difference in national mapping and LiDAR-scale carbon) ≤ 15.3 Mg C ha-1 in all six ecoregions (Figure 5).

The decision-tree approach for national upscaling provides a better result, with mean biases (net difference in national mapping and LiDAR-scale carbon) ≤ 15.3 Mg C ha-1 in all six ecoregions.

An example is shown in Fig. 4. First, the mean bias and mean difference between the seasonal amplitudes of the AOGCMs of the 20th century (years 1900 1999, Scenario 20C3M) and the historical observations at each location were found.

To this purpose we computed, for each grid point, the seasonal mean bias as the difference between the mean seasonal SLP values obtained from the CGCMs and those obtained from the NCEP/NCAR data.