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This conditional effect in a logit model is directly assessed through the "main effect" and its "interactive effect," given by the interaction coefficient [ 47].
Each photon packet produces a random walk whose step size is determined by an exponentially distributed random variable defined by the interaction coefficient, which is equal to the sum of the absorption µ a and the scattering µ s coefficients.
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The hypothesis that the progression rate is associated with the one year biomarker value was tested by assessing the statistical significance of the interaction coefficient.
For instance, as ε(Na+, AgCl2 -) is evaluated at 100°C 197°C, it might be possible to extrapolate the interaction coefficient to 75°C by using its linear expression.
To achieve this goal, experimentally determined mean activity coefficients and equilibrium quotients at various ionic strengths are compared with respective values predicted by using the interaction coefficients estimated by this study.
By design, non-linear models inherently include interactions, and as a result the interaction coefficient does not capture the full extent and complexity of the interaction effect.
The preceding combined model (i.e., craving, genotype, craving-by-genotype interaction) was significant (p <.001), neither craving, nor DRD4 VNTR coefficients were independently significant (ps >.30), and the interaction coefficient was significant (p <.05), reflecting the same positive relationship.
The slope is the interaction coefficient ε(H+, Cl-).
The interaction coefficient was tested using Wald χ2 statistics.
The interaction coefficient between fatal disease and policy implementation was 0.093, indicating a statistically significant interaction.
Therefore, according to the interaction coefficients recommended by this study and the equilibrium constant at infinite dilution, the equilibrium quotient at certain ionic strength can be predicted.
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