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The use of principle components analysis allowed us to characterize the neural bases of participants' behavioural performance more robustly and selectively than the use of raw assessment scores or diagnostic classifications because principle components analysis extracts statistically unique, orthogonal behavioural components of interest.
This study presents the first use of principle component analysis for identifying and successfully predicting the most important variables in the process of granulation and tabletting.
Wallis (1965) in discussion of multivariate statistical methods in hydrology recommends, for multifactor hydrological problems, the use of principle component analysis with varimax rotation of the factor weight matrix.
Compositional fields for BEA, ANA, MER, LTL, and pollucite (ANA-Cs), interactions between alkali cations in the templating process, and the use of principle components analysis to streamline the analysis of large X-ray diffraction (XRD) data sets are presented.
The use of Principle Component Analysis offered an opportunity to handle a set of correlated variables by reducing the dimensionality to a set of uncorrelated ones (i.e., principal components).
The use of principle components analysis rendered participants' scores on these three factors orthogonal and therefore ideal for use as simultaneous continuous predictors in a voxel-based correlational methodology analysis of high resolution structural scans.
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The purpose of this study is to explore the use of principle-based analytic tools to improve community knowledge building in a class of Grade 5/6 students.
However, classical logic does play a role in restricting the free use of principles of truth.
The use of principles of kinetics and modelling techniques to calculate parameters such as glomerular filtration rate.
Fig. 5 Difference between advanced and beginner student use of principles of natural selection in a long-response item, before and after instruction.
Some studies have had greater success at increasing student use of principles of natural selection in their explanations (e.g., Jensen and Finley 1996; Robbins and Roy 2007).
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CEO of Professional Science Editing for Scientists @ prosciediting.com