Table 6 is the

root mean square value of the tire dynamic loads.

RMSE] stands respectively for the

root mean square error of the power forecast of a wind farm.

With regard to results of each method performance in different events and two objective functions Peak-Weighted

Root Mean Square Error and Sum of Absolute Residuals, Green & Ampt method had better results than SCS and initial and constant rate methods and placed in first preference, SCS curve number and initial loss and constant rate methods placed in next preferences, respectively.

The air turbulence intensity, defined with the coefficient between air velocity

root mean square and the air velocity mean value, presented in this study, is calculated using the local mean air velocity value.

While table-3 gives the

root mean square error (RMSE) and mean bias error (MBE).

However, the PLS1 models with lower

root mean square error of prediction (RMSEP), standard error of prediction (SEP) and Bias were better when compared to the PCR models developed using the whole NIR spectra region and restricted NIR spectra region not associated with the hydroxyl band.

We calculated 3 commonly used SAECG parameters: hfQRS (ms), RMS40 ([micro]V), LAS<40 [micro]V(ms) and 6 new parameters: LAS<25 [micro]V(ms)--duration of the low amplitude <25 [micro]V signals at the end of QRS complex; RMS QRS([micro]V)--

root mean square voltage of the filtered QRS complex; pRMS([micro]V)

root mean square voltage of the first 40ms of filtered QRS complex; pLAS(ms)--duration of the low amplitude <40 [micro]V signals in front of QRS complex; RMS t1([micro]V)--

root mean square voltage of the last 10ms the filtered QRS complex; RMS t2([micro]V)--

root mean square voltage of the last 20ms the filtered QRS complex.

Three cross-validation statistics were reported, namely prediction error (PE), standardized prediction error (SPE), and

root mean square standardized (RMSS).

For the evaluation of the coincidence of the MODIS SST products and FT temperature measurements the

root mean square (RMS) difference (AT) of the satellite and FT temperature series was calculated as follows:

The

root mean square error (RMSE) was calculated for each model prediction using the following equation:

Several measures were used to assess the model fit: the Chi-square goodness of fit, the ratio of the Chi-square goodness of fit to the degrees of freedom, the

root mean square error of approximation (RMSEA), the normed fit index (NFI), and the expected cross-validation index (ECVI).

In this study, the Poisson estimate was found to adequately approximate normalized

root mean square errors (a measure of uncertainty) of counts for point measurements and profile measurements of water specimens.