Sentence examples for set of variable combinations from inspiring English sources

The foundational principle of this is to select a set of variable combinations from a number of scenarios while it is still adequate to build the relationship among the independent variables and the response at a certain confidence level.

The infiltration method opens a new horizon in the advanced electrode development as the method expands the set of variable electrode materials combinations with the elimination of thermal expansion mismatch and the suppression of potential detrimental reactions between electrode and electrolyte materials.

BMA allows the inclusion and exclusion of potential influences by means of a Markov Chain Monte Carlo (MCMC) samplerm investigating the whole model space, i.e. the set of all possible variable combinations that can be employed to represent the dependent variable.

One of these multivariate methods is the principal component analysis (PCA); this technique reduces the dimensionality of the data by explaining the variance-covariance structure of a set of variables using few linear combinations of these variables.

PCA explains the variance-covariance structure of a set of variables by using linear combinations of those [ 15].

On the other hand, using principal component analysis (PCA), variables are transformed into a new set of variables, which are linear combination of original variables.

In other words, a new set of variables is found as linear combinations of the measured variables so that the observed variations in the system can be reproduced by a smaller number of these causal factors.

It has the ability to describe the variation of a set of variables as a set of linear combinations of the original variables, so that successive linear combinations which explain most of the variation in the original data are extracted.

Principal components analysis describes the variation of a set of variables as a set of linear combinations of the original variables, in which each consecutive linear combination is derived so as to explain as much as possible of the variation in the original data, while being uncorrelated with other linear combinations [ 17].

In PCA, a set of variables is transformed into some linear combinations of the original variables by assigning weights to each variable so that the resulting composite variables as a set may have maximum variance under the restrictions that different linear composites are orthogonal to each other.

The new set of variables is derived from the linear combination of the original variables.