Sentence examples for most multivariate statistical from inspiring English sources
Owing to this fact, it is necessary to perform one or several data transformations in order to project them in Euclidean space, used by most multivariate statistical methods.
Most multivariate statistical monitoring methods based on principal component analysis (PCA) assume implicitly that the observations at one time are statistically independent of observations at past time and the latent variables follow a Gaussian distribution.
Various statistical methods relate soil Vis-NIR spectra to soil properties including partial least-squares regression (PLSR), the most common multivariate statistical technique in soil science.
40 Most studies conducted multivariate statistical tests (that is, with more than one predictor) to adjust the outcomes for factors other than ethnicity.
Most of the bivariate and multivariate statistical techniques have their drawbacks in making assumptions prior to investigation and sensitivity towards outlier values (Abrahart et al. 2008; Tehrany et al. 2013; Umar et al. 2014).
The logistic regression approach [ 15] is perhaps the most common technique used to develop multivariate statistical models to predict morbidity and mortality risk after coronary artery bypass grafting.
Both univariate descriptive statistics and multivariate statistical techniques were employed for the analysis of data.
Multivariate statistical methods are the most common and useful tools for exploring associations between responses and combinations of candidate stressors (Table 2) and can be used to help design studies.
PCoA is a multivariate statistical technique for finding the most important axes along which the samples vary.
The most prominent results were our ability to demonstrate, through multivariate statistical analysis, that the presence of EBV DNA at any level in both circulating PBMC and tumors was associated with increased lifetime for BC patients.
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].
