Sentence examples for using multivariate statistical analyses from inspiring English sources

To better assess land use influences, we developed empirical models relating land use patterns to the water quality of low-order streams at different geomorphic regions across multi-scales in the Dongjiang River basin using multivariate statistical analyses.

Due to the inherent quantitative nature of NMR, we were able to utilise this spectral information in two distinct ways; firstly for the classification and characterisation of samples using multivariate statistical analyses and secondly, the identification and subsequent quantification of metabolites present in the media.

Cortical borders were identified using multivariate statistical analyses of the laminar distribution of cell bodies (Schleicher et al. 1998, 1999, 2000, 2005).

We characterized differences in the metabolomic profiles of the three groups using multivariate statistical analyses: principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

Data will be analyzed using multivariate statistical methods which allow analyses of time-dependent outcomes and covariates.

The data was analysed using multivariate statistical technique (principal component analysis) to identify the hydrogeochemical processes which result in the variations in the chemical composition of groundwater.

The obtained values were analysed using multivariate statistical analysis, a quantitative method allowing interpretation of high-dimensional datasets.

Growth responses were recorded as colony diameter after one and two weeks of growth and analysed using different multivariate statistical analyses.

Then, four major TESEMA variables are used in multivariate statistical analyses to reduce the big data diversity.

Several landscape metrics related to patterns such as landscape fragmentation, connectivity of habitats and ecotone properties were computed at the class level for the new mosaic and were used in multivariate statistical analyses.

SIMCA-P +12 software (Umetrics, Umeå, Sweden) were used for multivariate statistical analyses (i.e., PCA and OPLS-DA) and the R statistical environment http://cran.r-project.org for other statistical analyses such as the cross-contribution compensating multiple standard normalization (CCMN) and calculation of a false discovery rate (FDR).