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In practice, the proposed collaborative design process will permit to develop an online recommendation system for garment size selection and fit estimation.
Values for k on and k off calculated from linear regression analysis are in good agreement with values obtained from the global fit estimation.
The aim of this was to reduce any over-variation and heteroscedasticity biasing fit estimation.
Our "goodness of fit" estimation method was the generalized least squares method (GLS) [ 6].
Then curve fit estimation was carried out to validate the regression model when significant correlation was fulfilled.
However, goodness of fit estimation by the Bayesian Information Criteria [ 29] suggests that additive models, including TATA-box and methylation, are better than one-factor ANOVA models.
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The final fit estimations (from MCMC) are the red curves in the first column.
(5) and (6), best fit estimations for k on and k off can be calculated by using multiple radioligand concentrations.
Taken together, all these confounding factors increase the uncertainty that is associated with the GT to ST fit estimations.
Best-fit estimations for k on and k off were obtained from Eqs. (5) and (6).
Best-fit estimations for k on and k off were obtained using Eqs.
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