Sentence examples for multivariate analysis such from inspiring English sources

These studies used multivariate analysis, such as factorial analysis and IRT models, to establish safe procedures to the instrument reduction, while keeping the same original dimensions.

Multivariate analysis, such as factor analysis (FA), discriminant analysis (DA) and cluster analysis (CA), aiming to interpret the governing processes through data reduction and classification, are recognized as powerful tools to deal with the increasing number of hydrochemical parameters.

Modelling latent variables at individual level using multivariate analysis (such as regression) in transport policy research has long been recognised as a robust way to gain insights into understanding relationships between multi-dimensional issues.

However, multivariate analysis such as principle component analysis (PCA) and cluster analysis is widely used to explain the correlation amongst a large number of variables in terms of a small number of underlying factors without losing much information (Meglen 1992; Ogwoeleka 2015; Pazand 2016; Qian et al. 2016).

A data set of the digitized spectra is analyzed by multivariate analysis, such as principal component analysis (PCA), and the pattern recognition contained within the spectra enables features of the metabolite profiles of the tissues to be extracted, even if all the metabolites detected are not assigned.

This suggested that a multivariate analysis such as random regression was more appropriate for these data.

Multivariate analysis methods, such as Clustering analysis and Principle component analysis, were applied to determine the quality differences between samples.

The concept is universally applicable to any multivariate analysis and such diagrams would be useful to comprehend the outcome on all variables at a glance.

Researchers have attempted to build models explaining the relationship between design variables of consumer products and affective user satisfaction using statistical multivariate analysis techniques such as linear regression and quantification theory.

For this purpose, spectrophotometric data have been analysed by multivariate analysis techniques such as Principal Component Analysis and Hierarchic Cluster Analysis.

Data have been analysed by multivariate analysis techniques such as Principal Component Analysis (PCA) and Hierarchic Cluster Analysis (HCA) with the attempt to better study and understand the colour conditions of the surface before and after the cleaning.