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Depending on the observed correlation between the dependent variables, MANOVA or MANCOVA (multivariate analysis of covariance) will be performed in sensitivity analyses.
Since functional connectivity is inferred from statistical dependencies between physiological measures of brain activity (i.e. correlations between the dependent variables), it can be estimated without an experimental manipulation.
IBA can deal with the relationship between the dependent variables by defining the control variables.
Perhaps the most interesting results in Table 1 are the correlations between the dependent variables.
The correlations between the dependent variables and independent variables is given in Table 4f.
Finally, the structure matrix (Table 4) is obtained, which explains the relationship between the dependent variables and the variate.
Coefficients represent associations, not causal relationships between the dependent variables and relative Medicare reimbursements, specialty availability, and PCP availability.
The advantage of ANNs over GLM is that ANNs can directly take into account any non-linear relationships between the dependent variables and each independent variable.
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Thereby, if the test was significant, there was a linear dependence between the dependent variable and the independent ones selected.
Multivariate logistic analysis however produced a statistically significant relationship between the dependent variable and the independent variable "ever told by a health worker about anticipated side effects".
Univariate logistic analysis yielded relationships of statistical significance between the dependent variable "reporting side effects from current contraceptive use" and 6 independent variables.
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