In this case study, multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), had been integrated to evaluate and interpret spatiotemporal variations of water quality in Xiangxi River, with a 5-years (2002 2006) continual monitoring data (14 parameters at 12 sites).
Different multivariate analysis techniques including principal component analysis (PCA), hierarchical cluster analysis (HCA) and partial least square discriminant analysis (PLS-DA) were combined to reduce the data sets into a series of optimized and interpretable objects.
Standard epidemiological data analyses are useful as first step like multivariate regression techniques, including Cox' proportional hazard analyses, and multi-level models.
Both the empirical approach and the multivariate statistical techniques, including principal components analysis (PCA) and linear discriminant analysis (LDA), were employed to develop effective diagnostic algorithms for differentiations between normal and dysplasia tissue in the stomach.
Both the empirical approach and multivariate statistical techniques, including principal components analysis (PCA), and linear discriminant analysis (LDA), together with the leave-one-sample-out cross-validation method, were employed to develop effective diagnostic algorithms for classification of Raman spectra between normal and dysplastic gastric tissues.
The fundamental advantages of geometric morphometrics over traditional approaches (i.e., multivariate morphometric techniques) include the development of powerful statistical methods based on models that are used to examine the shape variation of all configurations that correspond to morphologic landmark locations.
To express the mathematical relationship between dependent and independent parameters, nonlinear regression and soft computing techniques including multivariate adaptive regression splines (MARS), gene expression programming (GEP) and group method of data handling (GMDH) were utilized.
Then, some crucial quantitative thermo-kinetic parameters including growth rate constant, heat-flow power and heat output, etc. were obtained from theses curves and were further investigated by some powerful chemometric techniques including similarity analysis, multivariate analysis of variance, hierarchical clustering analysis and principle component analysis.
To date, metabolomics data have been limited to relatively standard statistical techniques including univariate analyses and multivariate analyses such as principal component analysis [ 1, 2].
