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The model contains only a single unknown parameter to be estimated from experimental data with even a more accurate description of the experimentally determined temperature profiles; on average 24% smaller sum of squared residuals than the two parameter model.

sum of squared residuals.

Summation of squared residuals.

All parameters in nonlinear models were estimated by minimization of the sum of squared residuals.

Among those fits that converged, we selected the one with the lowest sum of squared residuals.

RR: Relative risk; SSR: Sum of squared residuals; WHO: World Health Organization; ILI: Influenza-like-illness.

The genotype with the lowest sum of squared residuals is assigned to the window.

This is achieved by minimizing the sum of squared residuals weighted (SSRW).

Figure 4 shows the root-mean-square (RMS) errors (the square root of the average of the squared residuals between the true and estimated values) for the values of ϕ and α that minimize the evaluation function E for each iteration of the steepest descent method.

The constant α is a ratio by which the squared difference between the estimated and measured blood concentrations has more influence on the fitting process than the average of the squared residual errors of the modelled m tot(t) values.

The optimal values of convergence control parameter as well as the minimum values of total averaged squared residual error for various order of approximations is shown in the table.

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