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Several interactions between the factor variables were identified.
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The association between probability of offer and group of medical school applied to was evaluated by assessing for interactions between this factor variable and the sociodemographic predictors.
The interaction between risk factor variables in the final model was also investigated.
The main concern with structural validation of the URSSA is the high correlation between latent factor variables found in the four-factor model.
The paper also seeks to develop a statistically sound regression model that establishes the relationship between the factors (process variables) and the ethanol output (response variable).
In multivariate analyses, independent associations between the cardiometabolic risk factor variables and central obesity were examined for both groups using separate multivariate logistic regression analysis models, taking the presence or absence of central obesity (yes/no) as the dependent variable.
We found no interactions between ethnic origin and the risk factor variables with sum of skinfolds as the outcome.
The differences in the mean values (mean ± standard error) of the cardiometabolic risk factor variables between the sub-groups were tested separately for each of the two groups (normal weight and overweight/obese) by analysis of covariance, adjusting for age, race and sex wherever necessary.
Structural equation modelling represents a contemporary statistical technique for testing and estimating the relation between the factors and variables [91].
A frequency distribution of the correlations between all possible combinations of the 15 factor variables is shown in Fig. 2.
On the other hand, there are no notably high correlations between factor variables suggesting that the identified factors can be utilized as initial estimates for measures of patient characteristics.
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