Sentence examples for mean square variables from inspiring English sources

The root mean square variables, which were a measure of the undulations or noise in the signal [ 9], also increased with increasing disease severity.

In this work, a variable-tap length, variable step normalized least mean square algorithm with variable error spacing is proposed.

Scaling was performed by subtracting the mean then dividing by the root mean square for each variable.

The discrepancy in the trends of changes in 6MWD between study groups remained significant after adjusting baseline variables (mean square = 243.262, F index = 4.402, and P = 0.045).

The discrepancy in the trends of changes in 6MWD between study groups remained significant after adjusting baseline variables (mean square = 243.262, F-index = 4.402, and P = 0.045) (Table 3 and Figure 1).

It is derived by calculating the difference between the cross-validated model performance (out-of-bag mean square error; MSE) using all variables as model input and the performance of a model with permutated values within the respective variable, which enables a ranking of the most important variables by increase in MSE.

Our results show that AVE mean square root of each value variable is significantly greater than its correlation coefficient with other variables (Table 2).

Our results show that AVE mean square root of each value variable is significantly greater than its correlation coefficient with other variables (see Table 5), thus discriminant validity is supported.

The grouped analysis revealed one statistically significant comparison between barefoot trials for the root mean square greater than 60 Hz variable.

An adaptive and accurate estimation of the room responses is provided introducing a normalized least mean square optimization approach with a variable step-size, and taking advantage of an interchannel coherence reduction technique based on the missing fundamental phenomenon.

The BSS method and the Blind least mean square algorithm using Gray's variable norm as a measure of non-Gaussianity of the sources is briefly described and separation results for both simulated and measured data are presented and discussed.