Sentence examples for squares of deviation from inspiring English sources

The objective function is to minimize the sum of the squares of deviation between the actual and the estimated direct runoff hydrographs.

The least squares method minimizes the objective function which is the sum of squares of deviations of the actual and predicted direct runoff hydrographs.

The minimization of electrical distances between nodes is used to present the regulation efficiency, while the sum of squares of deviations (SSD) of real and reactive power reserve is used to present the regulation capability.

This consists of the ratio of the sum of squares of deviations from the mean image intensity (over the whole time series) due to the model to the sum of squares of deviations due to the residuals (SSQ ratio).

This consists of the ratio of the sum of squares of deviations from the mean image intensity due to the model (over the whole time series) to the sum of squares of deviations due to the residuals (SSQ).

This consisted of the ratio of the sum of squares of deviations from the mean image intensity (over the whole time series) due to the model to the sum of squares of deviations due to the residuals (sum of squares [SSQ] ratio).

The calculated predictive sum of squares of deviations (PRESS) values (see Table 12, last row) for 25 omitted compounds show that the LEO cross validation provides significant discrimination between the SBSP and empirical models.

A goodness-of-fit statistic [an sum of squares quotient (SSQ) ratio] was then computed at each voxel consisting of the ratio of the sum of squares of deviations from the mean intensity value due to the model (fitted time series) divided by the sum of squares due to the residuals (original time series minus model time series).

Individual activation maps were recalculated by testing the goodness-of-fit of this convolution with the blood oxygen level-dependent time series that used the ratio of the sum of squares of deviations from the mean intensity value due to the model (fitted time series) divided by the sum of squares due to the residuals (original time series minus model time series).

A goodness-of-fit statistic (the SSQ ratio) was then computed at each voxel consisting of the ratio of the sum of squares of deviations from the mean intensity value due to the model (fitted time series) divided by the that of squares due to the residuals (original time series minus model time series).

The average for each sub-set is for the sub-set with lower signals - for the sub-set with higher signals 2. The sum of squares of deviations for each sub-set: 4. We choose K i, for which is 5.