Sentence examples for multilinear regression of from inspiring English sources
The matrixes contain the coefficient estimates (R2 values) for a multilinear regression of the brain-to-brain entrainment response using as predictors the brain entrainment to speech of the listeners and the speakers.
The consistency of results has been analysed by a new method of multilinear regression of the quantity p aHγCl) as a function of both temperature and solution composition.
Specifically, we obtained the coefficient estimates (percentage of variance explained and associated p values) of a multilinear regression of the brain-to-brain entrainment results (at each channel pair) using as predictors the corresponding results from the brain entrainment to speech analysis (corresponding channels) for each of the 30 observations.
The consistency of the present results with the previously determined pHs values in 5, 15 and 30 wt% acetonitrile water mixtures is analysed by multilinear regression of pHs as a function of both solution composition and temperature.
Multilinear regression of soil properties combined did not result in any significant relationships.
Additionally, the genotypes of multiple densely spaced loci are usually correlated due to LD, which may induce collinearity of genotype vectors, and decrease the power of multilinear regression for MLAS [12].
Because of large degrees of freedom (dfs) in statistical tests, it is difficult to directly apply multilinear regression to large genetic regions for MLAS.
A set of tagSNPs capturing majority of the genetic information of candidate genetic regions, and having no or weak collinearity among each other, can be selected and included into multilinear regression for MLAS.
Multilinear regression is one of the vital method of quantitative structure activity relationship which is used for displaying linear correlation among a dependent variable Y (biological activity; MIC50) and independent variable X (physicochemical parameters).
Multilinear regression is one of the major multivariate analyses approaches, and has been applied to MLAS [12], [13].
We tested the association of labour characteristics and outcomes with CR and Bishop score by stepwise multilinear regression analyses, and the accuracy of prediction of categorical clinical outcomes by analysis of the area under the curves (AUC) of derived Receiver Operator Characteristic (ROC) curves.
