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Although the approach has limitations, including the requirement to merge the four socioeconomic position categories into two and to include not more than three age-groups, it does provide an indication of best fitting life course exposure patterns, although in some cases the distinction between the best fitting and the next best fitting models is not strong.
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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.
The best fitting model is shown in Fig. 5.
Therefore, the best fitting model is selected by relatively compared values over all indices.
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