Sentence examples for multivariate analytical approaches from inspiring English sources

Observational studies typically use multivariate analytical approaches, using maximum likelihood based modelling methods to try to adjust estimates of relative risk for these biases.

The chapter will describe examples of process and operational challenges that may be addressed using multivariate analytical approaches then provide guidance on how such approaches can be easily adapted into typical factory environments.

This study demonstrates that powerful experimental designs combined with univariate and multivariate analytical approaches are fundamental in distinguishing the subtle effects of human impact from those of natural processes.

This suggests that the activation of a cyclic pattern of behavior not only confers an advantage regarding temporal variability and accuracy in multiple interval tasks as reported before [5], [18] [20], but also may engage a distinctive neural substrate that can be discriminated from the single interval mechanisms using multivariate analytical approaches.

In each study site, edaphic and geographic variables were determined, and the bacterial structures were evaluated at the genetic and metabolic levels through fingerprint approaches along with multivariate analytical methods including redundancy (RDA) and variance partitioning (VPA) analyses.

The ever-increasing volume and complexity of molecular data from cancer, exemplified by the TCGA project, calls for more sophisticated analytical approaches that can detect multivariate relationships in heterogeneous data and map that information onto the existing body of knowledge of the structure of molecular networks that govern all cellular processes.

Analytical approaches have been extended to multivariate traits, characterized by a matrix of genetic covariances, with environmental change (continual or abrupt) implemented as a shift of optimum in multidimensional trait space (Gomulkiewicz and Houle 2009).

The study demonstrates that infrared spectroscopy combined with the multivariate analytical method principal component analysis and Gaussian peak fitting is an efficient approach to probing structural change in polymers during heating.

From a completely known system to a completely unknown system, analytical approaches for SRS imaging span from least-squares fitting to principal component analysis to multivariate analysis, as discussed below.

Analysis and reporting In the 34 scientific publications, various analytical approaches were used (Appendix 3, see Supplementary data, Table 4), varying from descriptive statistics, all studies, to multivariate techniques such as mixed linear modeling (Nerland et al. 2014, Robinson et al. 2013) and propensity modeling (Kasliwal et al. 2012, Adogwa et al. 2014).

The statistical approach must be considered during the design phase of any study and often involves the use of multivariate analytical techniques.