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This multivariate approach allows a considerable improvement in chromatographic performance using fewer experiments, without additional cost for columns or other equipment.
This is because implicitly, most estimation/training methods assume for the univariate approach that the variance of the error is constant whereas the multivariate approach allows for variation over the day.
The multivariate approach allows for inclusion of correlation across diseases and, consequently, increases the outbreak detection capability of the methodology.
The multivariate approach allows each of the individual paths to be estimated, and tested for statistical significance, however, it also allows groups of paths to be tested simultaneously, using the χ2 difference test – the multivariate F test in MANOVA is equivalent to the simultaneous test of all of the paths from the predictor variables to the outcome variables.
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These multivariate approaches allow for predicting the effect of a given factor at several levels of combinations with other factors.
Our approach allows for multivariate determinants and multi-location choices of net migration flows.
Such an approach allows an analysis of interrelationships among factors rather than making the strict statistical associations commonly found in multivariate methods [ 27].
This approach allows inductive category development.
This approach allows advanced patient privacy consents.
Multivariate statistical approaches allow deriving hidden information from the dataset about the possible influences of the environment on water quality (Spanos et al. 2003).
Multivariate, algorithmic approaches allow optimization of combinations of clinical variables to predict outcome.
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