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PDB wrote sections relating to the multilevel modelling and sample size components.
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This section provides the details of the physics-based model and sample calculations for the base-case input values defined in Table 1.
Figure 1 E-learning success model and sample matrix.
Particularly, annealing and Monte-Carlo techniques are used to minimize an error between correlation functions of model and sample.
We ran 500 simulations for each combination of residual variance, correlation, model, and sample size.
The experimental setup, Mie scattering modeling, and sample preparation are then examined.
Parameter estimates for single-population models generally improved (RMSE declined) with larger samples across the models and sample sizes simulated.
Sample 1 was used for initial testing of the structural model and Sample 2 was used for validation.
A multitude of similar experiments with different compartmental models and sample fractions are presented in File S1.
The variability will reflect patient heterogeneity, the imprecise nature of the particular clinical factors investigated, inadequacy of the Cox and log-normal modeling, and sample size.
As suggested by Hair et al. [ 13], an NC value larger than 1 and smaller than 5 should indicate fitness between the hypothetical model and sample data.
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