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Results of logistic regression models with interaction terms suggest that none of the variables interacted with condition.
Based on the multivariable models with interaction terms a decision model for treatment choice was developed.
*p values between regression slopes were obtained from linear regression models with interaction terms 13613_2018_391_MOESM2_ESM.tiff Additional file 2: Figure S2.
In this section, we present the results of the estimated flexible hierarchical logit models with interaction effects.
Next we investigated if models with interaction terms (models 10 and 15) would predict more accurately than the additive model.
In models with interaction effects, the assessment of statistical associations is done using combined direct and indirect effect estimates, along with joint tests of their significance.
Polynomial models with interaction terms were developed to study the influence of T and Qv on two responses: porosity and axial dispersion coefficient (ADC).
Statistical models with interaction terms were derived to evaluate influence of carbopol (X1) and chitosan (X2) on tablet disintegration (Y1) and dissolution (Y2), mechanical properties (Y3), swelling (Y4).
In the second stage (modelling), all data gathered in the experimental works were utilized to formulate first-order models with interaction.
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Sex-specific models and models with interactions between the JPI and ESS rounds (models (2) and (4)) on the total sample allow us to investigate this issue further.
Because species-area relationships may differ by feedstock, we also included models with interactions between patch size and feedstock type.
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models with relationships
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