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The principal component Analysis was performed as factor analysis using 14 biomarker ratios (variables) obtained from GC MS analysis of the 16 crudes oil samples from Nembe Creek Oil field, Kolo Creek Oil field and Afam Oil field (Table 2).
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ANOVA was used to compare interval and ratio variables across households by livelihood strategies and County.
The highest biosurfactant production was reached when high C/Fe and high C/P ratio variables were combined; biosurfactant concentration increased by a 3.42 fold.
The estimation results, presented in Tables 7 and 8, support the previous findings regarding the effects of ppp and wage ratio variables.
Comparison of composite PA ratio variables showed different range of variation between skin and seed.
Subsequently, the single proatherogenic measures were directly compared with the ratio variables.
The data elements are a mix of dichotomous and ratio variables.
It covers several original multivariate data analysis methods and applies nominal, ordinal, interval, and ratio variables.
Three categories of PA variables were assessed: total content, percentages of constitutive subunits and composite ratio variables.
Finally, when we included the two ratio variables in the model, there was little influence on the HRs.
These simple ratio variables can be regarded as process indicators instead of direct output variables and may help better understanding efficiency and productivity results.
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