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This is achieved by developing graph theoretic model, matrix models and variable permanent function model.
Here, different models and variable representations are used to generate maps of FTLE, and the resulting structures are applied to design and analysis within an astrodynamical context.
Bayesian mixed models and variable selection models were used and, based on parameter samples from the model posterior distributions, explained variances were further partitioned at the level of chromosomes and genome segments.
Minor comments In the introduction, the statement that mathematical models provide clear and precise hypothesis that are susceptible to contradiction and that failure to fit leads to rejection needs to be more nuanced, particularly in the case of biology where comparisons are often made between deterministic models and variable experimental results.
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All of the models and variables are significant at 95%% confidence level (not shown in Table 5).
Further details pertaining to models and variables are given in the sections that follow.
The variables tested in univariable models and variables inserted into the multivariable models are listed in Additional file 1.
The other models and variables lost their significance when adjusted for AP > 1.5 ULN and the CS nonresponse.
Univariate analysis was done to determine which variables to potentially include in the multivariable models and variables with p > 0.1 were then removed using backwards elimination.
The model and variable selection is key to multivariable analysis.
Model and variable selection were based on descriptive results and univariable regression for the outcome of fatality.
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