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To verify the proposed models, regression analysis has been conducted on the experimental and predicted solubility of carbon dioxide in ILs.
In Table 2 and in Figure 2A-G, the results of the mixed models regression analysis are shown.
In the mixed models regression analysis, these differences were corrected for by including the baseline values as a covariate.
To study the magnitude of this "inter-site effect" on the SEE more rigorously, we used Linear Mixed Models regression analysis (LMM).
In both models, regression analysis was adjusted for education, antihypertensive therapy and systolic blood pressure in the same year as cognitive assessment.
The effect of three investigated factors (F1: PCL-deficiency; F2: Knee flexion; F3: flexor muscle activity due to application of an extending torque) on the position of the reference points relative to the tibia were assessed by mixed models regression analysis using an exchangeable correlation matrix to take the occurrence of repeated measures into account.
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Mixed-model regression analysis was used.
Developmental trajectories were estimated using mixed model regression analysis.
Through multi-model regression analysis, optimum model and its parameters can be chosen to predict the production effect.
In developing the model, regression analysis to fit coefficients of the developed model is compared to the regression analysis results obtained from the experiment of the researchers.
Table 3 summarizes the association between the different case-mix variables, the stages of endometriosis, and surgical outcomes, using the mixed model regression analysis (univariate and multivariate analysis).
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