Examples of using Component analysis in English and their translations into Portuguese
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Download AIDA32 is a component analysis utility.
Main component analysis produced four factors Table 2.
The use of statistical analyzes such as principal component analysis(pca) and groupi.
Principal component analysis was performed to identify dietary patterns.
D centers, from which we do tensile test, bending test,hardness test, and component analysis.
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Principal component analysis(pca) was separately applied to all data set.
This unidimensionality has been confirmed by principal component analysis performed in several studies.
Principal component analysis was used to obtain the food consumption patterns.
Factors with an eigenvalue> 1 on principal component analysis were included in varimax rotation.
Component analysis was carried out using chromatographic techniques gc-ms/ gc-fid.
The application of principal component analysis(pca) showed that although the samples did not pr.
Patterns in macrofaunal distribution were observed through between class principal component analysis pca-c.
Focused Principal Component Analysis: a graphical method for exploring dietary patterns.
Multivariate techniques were used: principal component analysis and cluster analysis. .
Principal component analysis indicated the presence of four factors with eigenvalues.
Data were analyzed by means of describing data, main component analysis, factor analysis and multiple regression.
Principal component analysis(pca) was used to evaluate similar uses between species.
The identification of dietary patterns was performed by principal component analysis followed by varimax orthogonal rotation type.
Figure 1 Principal component analysis and parallel analysis on the polychoric correlation matrix.
It was also performed the pearson correlation test and principal component analysis(pca) in order to correlate the results.
The interval principal component analysis(ipca) is used in order to explore 1h and 13c nmr spectra.
Was adopted for the data processing,multivariate statistical analysis, principal component analysis(pca) and cluster analysis ca.
The chemometric analysis(principal component analysis, pca) obtained from the nir spectral data yielded differences between the c.
The data were submitted to descriptive statistics analysis, t test, andmultivariate statistical analysis main component analysis.
By principal component analysis of environments formed 11 groups with cumulative variance 57.10% in two components. .
The most important geometrical parameters selected by principal component analysis(pca) were o13c12, o1o2c3, c3o13c12c12a and c12c12ao1o2.
Principal component analysis of metabolites involved in energy homeostasis on flies carrying OreR and sm21 mitochondrial haplotypes.
Chemometric methods of hierarchy analysis(ha) and principal component analysis(pca) were applied to chromatograms(hplc) and spectra hplc-dad-uv.
Principal component analysis is a dimensionality reduction technique which identifies linear combinations that explain most data variations.
In this speci c case,we introduce the rpca(robust principal component analysis) techinic for rayleigh waves extraction, also known as ground roll.