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The green line (for illite/water fractionation) is the best-fit second-order polynomial with two fit coefficients based on the equation in ref. 35 that includes three fit coefficients.
In developing the model, regression analysis to fit coefficients of the developed model is compared to the regression analysis results obtained from the experiment of the researchers.
The simulation runtimes are comparable between the two methods (on the order of 5 15% difference for the problems tested) and the on-the-fly fit coefficients only require 5 15 MB of total data storage.
Table 3 Dark count rate fit coefficients.
The values of the fit coefficients for the dark count rate as a function of temperature depend on the inhibition percentage p (Table 3) since disabling a greater portion of cells corresponds to cutting out the tail of the distribution of count rate of Fig. 8, which is responsible for the increase of the average count rate values.
Constraining these factors to be in the same range (−1 to 1) allows us to compare directly the values of the fit coefficients.
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The linear regression fit, coefficient of determination (R2), and Pearson linear correlation coefficient (P) are shown for each comparison.
Linear regression fit, coefficient of determination (R2), and Pearson linear correlation coefficient (P) are shown for each comparison.
Linear regression fit, coefficient of determination (R2), and Pearson linear correlation coefficient (P), n = 10 independent biological samples.
The adequacy of the model was established with various descriptive statistics like lack of fit, coefficient of variation, predicted residual sum of squares, and adequate precision.
In all experimental data used, the calculated parameters were always statistically significant (Student's t-test, α = 0.05), the equations were consistent (Fisher's F-test) and the goodness of fit coefficient of determination was higher than 0.99.
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