Sentence examples for multivariate analyses such from inspiring English sources

Multivariate analyses such as multiple regression, factor analysis, and principal component analysis are defined as subclasses of the multivariate analysis model.

After little or no change is seen due to the rotation and mean estimation steps, the process is deemed complete and the superimposed coordinates for each individual can be used as commensurate variables that describe individual shape and can be subjected to multivariate analyses, such as principal components analysis used here.

In contrast to classical analysis of NMR data which involves time-consuming assignment prior to data evaluation, unsupervised multivariate analyses such as principal component analysis (PCA) [4] is extremely fast and can be applied to the data without manual pre-treatment.

To date no approaches for dealing with this issue for multivariate analyses such as PCA have been proposed.

Several candidate markers that failed validation in univariate and multivariate analyses such as Chrom19 deletion, Chrom8q amplification, and BAP1 and TP53 mutations had low prevalence ≤10%.

In addition, analyses of faecal egg counts have typically used univariate rather than multivariate analyses such as random regression that are appropriate when traits are correlated.

It is thus surprising that our data did not show any significant seasonal variation in both univariate and multivariate analyses though such variations had been noticed elsewhere.

Contrary to expectation, these papers found that, in multivariate analyses, conditions such as over-positive reporting of quality of life [ 91] or having a better appetite were indicators of shorter survival [ 54].

The correlation between variables (e.g. genes and metabolites) is also important for multivariate statistical analyses such as principal component analysis (PCA) and hierarchical cluster analysis.

The Primer-E package 146 is commonly utilized by microbial ecologists and allows for multiple multivariate statistical analyses, such as multidimensional scaling (MDS), analysis of similarities (ANOSIM), and hypothesis testing.

Some of the most used methods to group metabolites in samples include multivariate statistical analyses such as principal component analysis (PCA), hierarchical clustering analysis (HCA), and self-organization mapping (SOM) [ 102].