Sentence examples for squares of the errors from inspiring English sources
The estimation is done by minimizing the sum of the squares of the errors between the model and measured data.
The sum of the squares of the errors (SSE) method (Rengaraj et al. 2007) is the most common error function in use.
The parameter optimization was executed by minimizing the sum of squares of the errors between simulation data and measured data considering biomass, rhamnolipid and glucose.
Hybrid Fractional Error Function (HYBRID) error function was developed by Ng et al. (2002) to improve the fit of the sum of the squares of the errors at low concentrations by dividing it by the measured value.
If we assume independent errors in the two latter quantities, we can calculate the expected error in the entropy change by means of the law of error propagation – the root of the sum of the squares of the errors in enthalpy and free energy is 2.8 kJmol-1.
The value of the reflux ratio is obtained by minimizing the objective function presented in Equation 2, which represents the sum of the squares of the errors between the calculated and the desired molar flows: g 2 ratio of reflux = d A - d A * 2 + s B - s B * 2 + b A - b A * 2 (2).
Minimization of the sum of the squares of the error function is performed using the variable metric method (VMM), and the results are compared with those obtained by the conjugate gradient method (CGM) established previously in the literature.
This was the case, because the mean square values for the individuals significantly exceeded the mean squares of the error terms (Additional file 5).
It is defined as the percentage of the total variance explained by the logistic model (1-SSE/SST × 100, where SSE is the sum of squares of the error and SST is the total sum of squares).
As a result, the errors of approximately 64% of the dataset are ranged within 30 degrees, and the rms (root mean square) of the errors is 67.9 degrees.
In order to use directly the least squares method, we can define the objective function as the sum of the square of the errors, which should be minimized (e.g., Paláncz et al., 2008).
