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The graphs of C s /C f against C s (Langmuir model) and log(C f ) against log(C s ) (Freundlich model) were drawn.
Furthermore, the probability of each model and log Bayes Factors (LBF) were calculated using the formulations as follows: (1) Prob model i = mL model i / ∑ j n mL model j ; (2) LBF = ln (mL (model1)) − ln (mL (model2)).
Pseudo R of the final model was calculated as 1 − log L f)/log L i), where log L f) is the log-likelihood value of the final model and log L i) is the log-likelihood value of the initial model with the constant only.
A stepwise approach (exclusion p > 0.10 and inclusion p < 0.09) was used to refine the model, and log likelihood tests were used to create the final parsimonious model; with significant covariates parity, education, smoking, delivery mode, folic acid use, and preterm birth.
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Large numbers of zero responses are common in WTP studies, and are found here; therefore, the Heckman selectivity model and log-transformed OLS are employed.
The experimental solubility data of solute in mixed solvent system were measured experimentally and further correlated with the calculates values obtained from exponent model and log-linear model of Yalkowsky.
Survival analyses used the Kaplan Meier model and log-rank tests.
Using standard values of these parameters, the equation is validated and converted to the following: P_{{{text{cwo}}_{text{reservoir}} }} = P_{text{cHgair}} times 0.1124 (20) Open image in new window Fig. 6 Water saturation derived from MICP data (black-dotted) using buoyancy model and log-based water saturation (blue line).
As shown in Fig. 2, the curves of different lithologies have sudden changes; the reason would be both the theoretical model and logging data.
Factors were compared using the Cox proportional hazards model and log-rank tests.
However, the P-values for Cox model and log-rank tests were barely significant.
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