Sentence examples for signed error from inspiring English sources
The position data, collected by a motion system, were used to compute accuracy, signed error, movement time, and throughput.
Significance was calculated on the average signed error over the whole volume across subjects using a one-sided paired Wilcoxon signed rank test.
Mean signed error can be useful to identify the tendency for under- or over-estimation of elevations (i.e. bias), while RMSE represents the overall mean elevation accuracy of a DTM.
This observed difference between elevation classes became larger with increased levels of data thinning; the mean signed error difference between submontane and montane areas with 20 returns m−2 (0.31 m) increased to 2.64 m when data density dropped to 1 return m−2.
For BMI signed error, we find that ethnicity (B = 0.163, p < 0.05), measured BMI (B = 0.163, p < 0.05), and time since last weighing (B = 0.419, p < 0.05) are significant, as increasing BMI and time since last weighing predict greater underreporting of BMI, while Ladinos underreport BMI more than Maya individuals.
We carried out the analysis on the absolute errors, signed estimation errors and standard deviation of absolute estimation errors.
The GLM analysis determined how mesencephalic dopamine regions encoded experiential signed TD errors, fictive error learning signals (Fig. 2 below), and unsigned TD errors (Fig. 3 below).
We first related signed TD errors, unsigned TD errors, and fictive errors over gains to task behavior.
We additionally performed a native-space within-subjects analysis to examine the discriminability of sources for signed TD errors, unsigned TD errors, and fictive errors, and also to test for directionality differences between the signed and unsigned TD errors.
We investigated the behavioral impacts of signed TD errors, unsigned TD errors, and fictive errors over gains on task behavior by relating each signal to the change in bet from the current to the next trial.
