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Several multivariate techniques are applied to interpret the grinding processes.
Multi-phenotype analysis has drawn increasing attention to high-throughput genomic studies, whereas only a few applications have justified the use of multivariate techniques.
In this study an innovative statistical procedure based on multivariate techniques is applied.
So it is needed to use multivariate techniques in that cases.
Data were analysed using multivariate techniques and partial least squares regression.
Patterns of assemblage composition were compared with patterns of environmental variables using univariate and multivariate techniques.
Some applications of other multivariate techniques for optimization of chromatographic methods are also summarized.
They show that non-linear multivariate techniques yield reproducible results, outperforming linear approaches.
Results can be relevant for landscape ecologists dealing with many variables and multivariate techniques.
Based on 42 surveys we analyze 16 quantitative indicators using multivariate techniques.
Data was analyzed using multivariate techniques (TWINSPAN, Detrended Correspondence Analysis, Cluster Analysis, and Kruskal Wallis test).
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