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Identification of structure property relationships between material surface chemistry and biological response can be achieved using multivariate analysis (MVA).
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Different second-order algorithms were tested and successful resolution was achieved using multivariate curve resolution-alternating least-squares (MCR-ALS).
The optimization of critical method parameters, such as the SPD reaction, was achieved using statistical experimental design and multivariate data analysis.
Also, the correlation between them needs to be reflected, and this is achieved using copulas (described later) to preserve the multivariate relationships.
Pairwise comparisons were achieved using Mann Whitney-U test.
This was achieved using the following procedure.
This was achieved by using multivariate statistical tools, which identified markers (metabolites and genes) correlating with a particular genotype.
Size-adjustment of BMC was achieved by using multivariate regression with the variables transformed to natural logarithms (9).
We show how a multivariate calibration can be achieved with the use of two weeks long on-field data recording and neural regression systems.
This is achieved by (i) using the conditional expectation formula for multivariate normal variates and (ii) using the correlation structure from a relevant reference population.
Data interpretation was achieved with a multivariate statistical tool.
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