Sentence examples for multivariate evaluation from inspiring English sources

A novel approach to multivariate evaluation of separation electrolytes for micellar electrokinetic chromatography is presented.

The concept of statistical experimental design and multivariate evaluation was shown to be a useful approach in cell toxicity screening studies.

In this study, we report the use of multivariate evaluation to optimize the formulation factors for the preparation of alginate-zinc nanoparticles by ionotropic gelation.

The influence of various formulation factors on the in vitro cellular toxicity of liposomes on human buccal cells (TR146), were studied by using the concept of statistical experimental design and multivariate evaluation.

Multivariate evaluation and modelling were performed starting with a principal component analysis (PCA) and followed by partial least square regression analysis (PLS) to assess both qualitative and quantitative influence of the investigated factors in the LN.

Generally all values were logarithmized and entered the multivariate evaluation as continuous variables.

If the statistics consistently underreported suicide, the methods could be reliable enough to justify multivariate analyses of determinants and multivariate evaluations of interventions.

This paper introduces a new approach to multivariate yield evaluation based on a numerical integration procedure called Gaussian quadrature reduction (GQR).

Current procedures for multivariate yield evaluation, such as Monte Carlo simulation, require substantial computing effort, making the iterative adjustment of design parameters often impractical.

The advantage of this approach is a large reduction in the computational burden associated with multivariate yield evaluation with virtually no loss in accuracy of the estimates.

The modeling procedure includes characterization of the population being studied, identification in advance of dependent or outcome variables and independent or risk factors, specification of the modeling plan including univariate and multivariate analyses, evaluation of the model predictive performance, and the procedure for validation of the model.