Sentence examples for multivariate data analyzing from inspiring English sources

In this paper, we introduce a new multivariate data analyzing technique, the correspondence analysis, to analyze the high dimensional microarray time-course data in case control design.

For more unbiased interpretation and to reduce the dimensionality of the multivariate data, we analyzed the LC-MS chromatographic data using principal component analysis (PCA).

Further they are less susceptible to overfitting and multicollinearity, provide more accurate predictions, and do not make the unlikely assumption that the multivariate data being analyzed are multivariate normal.

Data analyzed by multivariate logistic regression analyses controlling for the confounding effects of age and sex.

Any highly multivariate data can be analyzed by PGC by reducing the problem to a number of simple bivariate (pairwise) cases.

Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data.

Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.

Some have been devised for highlighting parallelisms among different time series when analyzing multivariate data, whereas others were designed for bringing the periodic nature of time into the spotlight.

PCA is a procedure for analyzing multivariate data that establishes "metric" differences between groups under investigation on the basis of their characterizing multivariate data [72].

Algorithms for analyzing multivariate data developed for other applications are currently used extensively in systems biology.

The parametric MANOVA and T test statistics are commonly used for analyzing multivariate data.