Sentence examples for mean percentage errors from inspiring English sources

In prior benchmarking, the method has yielded affect intensity mean percentage errors of 5% or less on similar Dark Web forums [36].

Repeated-measures ANOVAs were conducted on mean correct reaction times and mean percentage errors for matches and mismatches separately.

Repeated-measures analyses of variance (ANOVA) were conducted on mean correct reaction times (RTs) and mean percentage errors for matches and mismatches separately.

The models accurately predicted maximum ventricular pressures and volumes, not used in the identification process, to mean percentage errors of 7.1% and 6.7% (less than measurement error ~10%).

Results show that the proposed method improves the mean percentage errors by at least 5.7% on average hourly load profile.

The mean percentage errors for assessment of volumetric heat capacity and thermal conductivity were 4.6% and 8.9% respectively, computed after sensor spacing calibration in agar stabilized water.

The performance of the fuzzy predicting model has been compared with the response surface (RS) model with respect to mean squared error (MSE), mean percentage error (MPE) and co-efficient of determination (R2).

The contrast of statistical methods is compared through relative percentage error, root mean square error, mean percentage error, mean absolute percentage error, Chi-square error and analysis of variance (or) efficiency of the methods used.

In statistical analysis, for judgment of statistical methods to each other and to find out the efficiency of the methods, six statistical tools, i.e., relative percentage error (RPE), root mean square error (RMSE), mean percentage error (MPE), mean absolute percentage error (MAPE), Chi square error (χ 2), and analysis of variance or efficiency of the method (R 2) were used.

Estimated values were also compared with measured values in terms of statistical evaluation indicators like the coefficient of determination ((R^{2}),) mean percentage error, mean bias error, root mean square error (RMSE), mean absolute relative error, and t statistic.

These quantitative indicators are: the coefficient of determination (R 2), mean percentage error (MPE), mean bias error (MBE), root mean square error (RMSE), mean absolute relative error (MARE), and t-statistic (t-stat).