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Two multivariate methods including principal component analysis (PCA) and hierarchical clustering analysis (HCA) were utilized in this study.
If necessary, multivariate methods including Cox regression may be used to adjust the baseline differences.
If necessary, multivariate methods including analysis of covariance may be used for adjustment of baseline differences.
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Examples of multivariate methods include correlation-based feature selection and Markov blanket feature selection.
In this paper, we propose a robust decision support tool for detailed production planning based on statistical multivariate method including principal component analysis and logistic regression.
Mehrjoo and Bashiri (2013) proposed a robust decision support tool for detailed production planning based on statistical multivariate method including principal component analysis and logistic regression.
Multivariate statistical methods including factor analysis (FA) have been used successfully in hydrochemistry for many years (Praus 2005).
Multivariate statistical methods including BEST and LINKTREE routines were used to identify correlative relationships between different ecological variables.
Multivariate statistical methods, including unconstrained and constrained analysis, were applied to examine the relationships between organisms and the water quality as well as physical factors (including plant cover).
In each study site, edaphic and geographic variables were determined, and the bacterial structures were evaluated at the genetic and metabolic levels through fingerprint approaches along with multivariate analytical methods including redundancy (RDA) and variance partitioning (VPA) analyses.
The result of this study supports the fact that multivariate statistical methods including CA and PCA/FA can be applied to interpret complex datasets of heavy metals in soil, understand spatial variation in of heavy metals along roadside areas, and identify latent pollution sources/factors.
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