Sentence examples for squares of the deviations from inspiring English sources

At the same time, the function G expresses the sum of the squares of the deviations for all values over an hour, while the function A is calculated for one baseline value per hour.

The ratio between the sum of squares of the deviations of voltage magnitudes from 1 p.u. before and after including DG in the network is considered as voltage improvement factor.

end{aligned} (5)This function represents the squares of the deviations of function G from the expecting values of function F. If a minimum is found, G is adapted for approximation of F. Training or adaption of the network weights (w_{t}) is performed on training data set.

A non-linear regression algorithm of LM was utilized to fit the rival rate expressions to the experimental results by minimizing the summation of the squares of the deviations as follows and estimation of the reaction rate constants: f i, obj = ∑ i = 1 N r i exp - r i cal r i exp 2. (9).

Variance in log2 transformed expression levels was calculated across all 54,675 probe sets in the total distribution as the average of the squares of the deviations from the mean.

The MSE was defined as the sum of the squares of the deviations from the mean.

The data was fitted to the model using least squares error minimization, i.e., the sum of the squares of the deviation of the data point and the predicted value was calculated and the parameters b, c, k1, V1, k2 and V2 were chosen to minimize this sum of squares.

These fit indexes are both based on mean squares of the deviation between the data and the model, the only difference being that the infit statistics is a weighted estimate where persons close to the item location is given more weight than those at the tails of the distribution.

Reduced Chi-square is the mean square of the deviations between the experimental and calculated values for the models and is used to determine the goodness of the fit.

The method of least squares finds the values of a and b that minimise the sum of the squares of all the deviations.

Similarly, in quadratic loss function the loss is assumed to be proportional to the square of the deviation of the attribute from the best value of the attribute.