Sentence examples for by multivariate statistical techniques from inspiring English sources

After 11 summer weeks, there were significant differences in community structure between nutrient enrichment levels for both algal and animal assemblages when examined by multivariate statistical techniques.

Characterization and evaluation of surface and freshwater quality performed by multivariate statistical techniques has proved to be useful in verifying spatial and temporal variations caused naturally and due to human induced factors also (Helena et al. 2000; Singh et al. 2004, 2005; Hassen et al. 2016).

A set of quantitative descriptive and analytical data from the wastewater treatment plant (WWTP) of Sfax, located on the south-east of Tunisia, has been processed by multivariate statistical techniques in order to investigate the evolution of the wastewater quality over a period of 12 years (1984 1996) for six physico-chemical and biological parameters.

However, a detailed comparison among different species used as "Qin-Jiao" and "Long-Dan", and their related adulterants by applying multivariate statistical techniques is lacking.

Herein, we profiled and compared three "Long-Dan" species, four "Qin-Jiao" species and five adulterants by applying multivariate statistical techniques to their HPLC data sets to establish the differences and/or similarities.

The main aim of this paper is to provide a clear illustration of how large data sets from monitoring stations can give information about the number and nature of the pollutant sources, and mainly to assess the contribution of the traffic source to PM10 concentration level by using multivariate statistical techniques.

These results were analyzed by using multivariate statistical techniques (principal component analysis (PCA) and cluster analysis (CA)).

(As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn) by hydrochemical and multivariate statistical techniques, such as cluster analysis (CA) and principal component analysis (PCA).

In this study, metal pollution and their sources in surface soils were evaluated by pollution indices and multivariate statistical techniques in association with a geographical information system (GIS).

For the present study, farm household typologies were identified by using two sequential multivariate statistical techniques: principal component analysis (PCA) and cluster analysis (CA) (Ding and He [2004]).

As VCQ indicator, the NDVI-based vegetation cover classification was produced by means of unsupervised multivariate statistical techniques and compared with ancillary cartographic layers, statistical indicators, and field data related to land-use management observed in the study area.