It is worth noting from the multivariate response model that the socioeconomic variables of interest had stronger associations with physical functioning (accounting for 24% of variation) than psychological well-being (accounting for 7% of total variation).
A new method of processing multivariate response data to extract chemical information has been developed.
The optimization of the extraction conditions was carried out using multivariate response surface methodology.
Hard modeling approaches of fitting multivariate response data are based on mathematical relationships, which describe the measurements quantitatively [14]‐[16].
In the current experiments, the dependent AEP (or HGB power) measurement was treated as a multivariate response and assumed to be sampled from a multivariate distribution [34].
Designs for multivariate responses can likewise be found, requiring only the appropriate information matrix.
Some new results in the theory of optimum experimental design for multivariate responses are presented.
Weighting addressed these response variations.
Modeling conditional independences in multivariate responses is an open problem.
This analysis extracted the first and second eigenvectors that summarized multivariate quality variation and best represented "quality components" [72].
