Sentence examples for use of multivariate statistical from inspiring English sources

The use of multivariate statistical methods allowed for the simultaneous characterization and grouping of tested lines in terms of several traits.

Researchers working on the problem have been slow to develop or adopt the use of multivariate statistical techniques, despite their successful implementation in other archaeomaterials sourcing frameworks.

However, the use of multivariate statistical technique and pollution evaluation index to assess the safety and quality of hand-dug well water in the Ejisu-Juaben Municipality is lacking.

Inter-relationships amongst the analyzed water quality parameters, with special emphasis on relation of fluoride with the rest of the parameters have been studied with the use of multivariate statistical analysis tools in this study.

Correlation, either partial or complete, is inherent in such mixture data and requires the use of multivariate statistical tools for visualization and identification of important relationships in the data.

Several published research studies have employed the use of multivariate statistical analysis in the interpretation of groundwater quality data obtained from various sources (Sundaray 2010; Singh et al., 2008; Uddamari et al. 2014; Oketola et al. 2013; Molla et al. 2015; Arslan 2013; Zhang et al. 2014; Majolagbe et al. 2016; Markic et al. 2015; Razmkhah et al. 2010).

Recently quantitative spectroscopy has been greatly improved by the use of variety of multivariate statistical methods [37 39].

The hypotheses were tested using the support of multivariate statistical analysis techniques (in the analyses reported, both the SPSS software version 20.0 and LISREL 8.80 were used).

In this chapter we show how information can be extracted from these data sets using a class of multivariate statistical methods called latent-variable (LV) models.

Our aim in the present analysis was to ascertain whether the primary immune response to FIVC was affected by the presence of PLV using a suite of multivariate statistical methods that are suitable to explore data for typical experimental infection studies; those with small sample size, missing values, and co-variation among measured parameters.

CEM generated weights were used as covariate to adjust results of multivariate statistical modeling of outcomes [ 16].