Sentence examples for multivariate and complex from inspiring English sources

In the last years, multi-way analysis has become increasingly important because it has proved to be a valuable tool, e.g. in interpreting data provided by instrumental methods that describe the multivariate and complex reality of a given problem.

Depending on the collection method and desired individual, paired, and group variables, the data are often multivariate and complex.

Bi-linear methods based on estimated latent variables, e.g. Principal Component Analysis (PCA) [ 21, 22] and Partial Least Squares Regression (PLSR) [ 23- 25] (see Additional file 1: Appendix 1), have shown considerable success in analysis and interpretation of a large number of highly multivariate and complex data sets.

However, parameter estimation of PEMFC models is highly challenging due to their multivariate, nonlinear, and complex essence.

Diagnostic algorithms using multiple biomarkers and complex multivariate data analysis have been investigated the most in oncology.

Descriptive statistics and multivariate analysis were done and complex sample design used to take into consideration the design effect.

The evaluation of the DCE involved descriptive methods (frequency tables, statistics of distribution), bivariate procedures (cross tables, comparisons of means, ANOVA), and complex multivariate procedures (factor analysis [PCA], probit models, logit models).

To address the large number of variables within this database, PCA was employed as it allowed us to examine associations in large and complex multivariate databases that requires no a priori assumptions about the data and is thus ideal for exploratory data analysis (15).

This study presents the necessity and usefulness of multivariate statistical assessment of large and complex databases in order to get better information about the quality of surface water, the design of sampling and analytical protocols and the effective pollution control/management of the surface waters.

The uncertainty of calibration is analyzed using the Monte-Carlo method, whereas the uncertainty of the DAS beamformer is analyzed using both the complex multivariate and the Monte-Carlo methods.

However, most global health delivery (GHD) projects are far more complex, multivariate, and non-linear in reality.