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Bivariate analysis applying the dichotomized dependent variable OHRQL (OHIP-14) with the independent variables, showed statistically significant results for all variables except for marital status and age.
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To compare numerical variables in the two groups, data were analyzed in a bivariate analysis by Fisher's exact test and χ analysis applying the Yates' correction, 2 × 2 contingency tables.
Bivariate analysis was applied to calculate the predictive values of each variable.
Bivariate analysis was applied to plasma adiponectin with the metabolic syndrome components and metabolic syndrome related components using sex, age, and BMI as covariates (model A) or using sex, age, and metabolic syndrome as covariates (model B).
For each trait, we then applied a weighted bivariate analysis of the direct genomic breeding values of genotyped animals from all five validation sets and their corresponding deregressed estimated breeding values to estimate variance and covariance components.
Logistic regression was applied to further analyze the relationships between significant socio-demographic factors in the bivariate analysis (p<0.05) and the various aspects of the women's knowledge and practice about malaria.
Descriptive and bivariate analysis methods were applied, followed by multivariate techniques (including factor analysis).
In the bivariate analysis, the sire covariance was constrained to zero.
Section "Data and bivariate analysis" describes the data and provides descriptive statistics.
We then perform bivariate analysis on the traits HDL and TG.
A descriptive, bivariate analysis of the results was carried out.
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