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Process analysis showed that a quadratic model is adequate to describe methyl chavicol concentration using short path evaporation and that evaporator temperature was the unique statistically significant variable of the process.
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Significant variables of the models and craniofacial structures of each group were identified by one-way analysis of variance (ANOVA) at a significance level of P < 0.05.
At each iteration, this inserts the significant variables of the model.
Post hoc tests with Bonferroni correction (α=0.05/4=0.01) were executed for the significant variables of the within-subjects effects of Time.
All significant variables of the univariate analysis were included in the first run of a multivariable Cox proportionate hazard regression.
These were inter-adjusted and were also adjusted for the statistically significant variables of the hierarchical level immediately above.
Statistically significant ORs are bolded *Adjustment for age, survey year, age at menarche and the significant variables of the previous model.
Attributes for the significant variables of the final model were extracted for the set of point locations using the same methods as for the survey points.
Rather than building a new classification tree, we decided to keep the significant variables of the different components of cost (medical, nursing, rehabilitation, logistic) in an additive way.
We additionally performed a multivariate analysis with all significant variables of the univariate analysis, showing PADUA as independent predictor (OR = 2.197, CI = 1.294 3.731, p = 0.004).
(1): Multivariate logistic regression analysis with dependent variable the General Health Index of GHQ-28 and independent variables the major demographic variables and the statistically significant variables of the univariate comparisons.
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