We first present principal component analysis, which is a classic and well-known multivariate procedure that can be used as a dimension reduction strategy.
The general linear model multivariate procedure assumes that all model factors are fixed, i.e., they are generally thought of as variables, the values of interest of which are all represented in the data file, usually by design.
Our multivariate analysis also identified a number of conditions and surgical procedures that were independent predictors of long-term mortality.
In addition, multivariate analyses allow for the joint analysis of CpG-genetic and epigenetic structure through statistical procedures that maximize and test the common variance of different datasets.
PCA involves a multivariate mathematical procedure that transforms a number of correlated variables into a smaller number of uncorrelated variables called principal components.
Both approximate multitrait two-step procedures that used either YD or drEBV led to results that were comparable to those of the full multivariate animal model (MULTI).
Factorial design is a multivariate data analysis procedure that can be used to optimize the manufacturing processes of lipid nanocarriers, providing valuable information and minimizing development time.
One major reason for use of multivariate procedures for our model is that it keeps Type I error to a minimum (Fan, 1997; Sherry and Henson, 2005; Thompson, 1985 , 1991.
In conclusion, we have previously applied different clustering techniques with validity indices, and used cluster consensus to derive a classification that is robust across different multivariate procedures.
Univariate statistics were used to determine the total number of significant factors, and multivariate procedures were used to determine a smaller set of questions that retain predictive validity.
