Sentence examples for complex data matrices from inspiring English sources

In this present study, multivariate statistical techniques such as CA, PCA and FA were applied to the interpretation of complex data matrices to better understand the water quality.

The application of statistical analysis thus helps in the interpretation of complex data matrices to better understand the water quality as well as identify the possible factors that influence the water chemistry in a region.

The application of different multivariate statistical techniques, such as principal component analysis (PCA), assists in the interpretation of complex data matrices for a better understanding of water quality of a study area.

The complex data matrix (17,790 observations) was treated with different multivariate techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis (DA).

The application of different multivariate statistical approaches for the interpretation of a large and complex data matrix obtained during a monitoring program of surface waters in Northern Greece is presented in this study.

Preprocessing of the mobility spectra and the effectiveness of multivariate curve resolution techniques (MCR-LASSO) improved the accuracy of the measurements by correcting baseline effects and adjusting for variations in drift time as well as enhancing the signal to noise ratio and deconvolution of the complex data matrix to their pure components.

Therefore, a complex data matrix should be assessed to evaluate water quality (Chapman 1992).

Range-spread target detection is based upon the received complex data matrix composed by the complex HRRPs collected from a train of coherent pulses.

This resulted in a huge and complex data matrix comprised of a large number of physical-chemical parameters, which are often difficult to interpret and draw meaningful conclusions.

The analysis of hundreds of biological replicates of an organism under different controlled environmental treatments in the natural environment or with genotypic variation will result in a complex data matrix.

First, the simple model of sequence evolution used in the simulations yields alignments that are not as complex as empirical data matrices.