Sentence examples for multivariate analytical techniques from inspiring English sources

Multivariate analytical techniques were used to analyse the data and to develop detailed understanding on build-up.

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

The number, variety and spatial variability of the organisms encountered are presented and analysed using a suite of standard univariate and multivariate analytical techniques.

Multivariate analytical techniques represent a variety of mathematical models used to measure and quantify an exposure disease or an exposure outcome association, taking into account important factors that can influence this relationship.

For this reason, multivariate analytical techniques were used to categorize participants according to groups of related reasons.

This approach especially gained popularity in metabolic profiling where it is often applied to detect new (bio) markers in large data sets by means of multivariate analytical techniques.

The obtained matrix of hydrogeochemical data was subjected to multivariate analytical technique.

A class of multivariate data analytical techniques called multi-way analysis encompass techniques that have been designed to handle and analyse such data structures directly.

By using a strategy where multivariate data analytical techniques are used in conjunction with statistical experimental design to select a balanced set of compounds for break-through performance evaluation, it was possible to develop QSAfR models with high predictive capability.

This article is the eighth in a series exploring the importance of research design, statistical analysis, and epidemiology in nutrition and dietetics research, and the second in a series focused on multivariate statistical analytical techniques.

Principal component analysis (PCA) is a widely used multivariate analytical statistical technique that can be applied to data to reduce the set of dependent variables (i.e., attributes, traits) to a smaller set of underlying variables (called factors) based on patterns of correlation among the original variables [ 38].