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The models calculated for the MP set with excluded decomposing molecules calculated a lower CV RMSE and also a lower RMSE for predictions of the COMBINED set molecules.
Table 8 shows the variance of the estimated fixed effect (hat{beta }_3) across scaling models calculated for each country separately.
Table 1 shows a description of the variables that were included in the (zero-truncated) negative binomial regression models calculated for the LTF applicants.
The combined model was consistent with the models calculated for each mental health outcome, and is illustrated in Fig. 1.
The results of the logistic regression models calculated for women and men on two outcome variables suggest that the selected socio-economic determinants used in this analysis are important for women and for men in a differential manner.
In sum, the results of the logistic regression models calculated for women and men on two outcome variables suggest that the selected socio-economic determinants used in this analysis are important for women and for men in a differential manner.
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Table 3 indicates the performances of consensus models and individual sub-models calculated for the different number of excluded outlying molecules as described in the Methods section.
We present a few results of this model calculated for Iapetus.
In a multivariate logistic regression model calculated for prediction of septic shock the Odds ratio for S1P was 0.97 (CI 95% 0.96-0.99) and the best predictor of shock (P < 0.01) among all parameters tested.
Statistics were calculated for the ordinal regression model calculated for each physician.
In a multivariate logistic regression model calculated for prediction of septic shock, S1P emerged as the strongest predictor (P < 0.001).
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