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The best fitting models were logarithmic but they accounted for less than 50% of the variance.

The best fitting models were used to identify the values of the intrinsic thermal conductivities of DPF and mortar.

The best fitting models were selected by means of a Bayes factor (marginal likelihood) implemented in BEAST [29].

The best fitting models were determined through quadratic regressions.

When the best fitting models were identified, PAUP was used in order to find the maximum likelihood tree topology.

Subgroup analyses per gender are reported in Tables 3 and 4. Unsurprisingly, best fitting models were different.

Model building with various AFT models and results of the best fitting models are summarized in Tables 3 and 4, respectively.

The best fitting models were selected after stepwise regression analysis, i.e., those with significant properties and coefficients that account for functional or genomic properties.

Thus, based on all fit indices, the best fitting model was retained in all following analyses.

In confirmatory factor analysis, the selection of the best fitting model was based on several fit indices.

All models were fitted separately to each data set, and the best fitting model was selected for each chemical according to a statistical goodness-of-fit criterion (Akaike information).