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Logistic regression was used to build the adjusted model.
Logistic regression was performed using all factors associated with obesity in an initial unadjusted analysis and likelihood ratio tests were used to build the adjusted model.
Logistic regressions were performed using all factors associated with activity in an initial unadjusted analysis and likelihood ratio tests were used to build the adjusted model.
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The methodology for processing and analyzing the quality of the data-sets and the procedures to build the adjusted BRL model is thoroughly described.
The adjusted model was built manually and included all variables associated with hardship at the level of P <0.25 in univariate analysis (shown in Table 1).
Next, the covariates of the mean regression of the adjusted model were entered as moderators in the covariance matrix the same way the mean regression of the adjusted model was built.
Hence the adjusted model states are dynamically more consistent with those of the base model.
The adjusted model proves to be an accurate predictor of the mass burning rate.
Experiments with slow heating and cooling were used to validate the adjusted model.
The adjusted model included as covariates the potential confounders of education, ethnicity and self-reported health.
No additive retained its significativity in the adjusted model.
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