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In this work it is demonstrated that multivariate, data based statistical methods play a critical role in providing solutions to these issues.
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Spectral data can be used to classify or estimate properties of unknown samples when combined with multivariate data analysis based on a correlation with physical properties obtained from reference analytical methods applied to well-characterized and representative samples [23 26].
During the last years several groups have published data based on multivariate logistic regression analysis to describe characteristics of patients who needed readmission to the ICU.
MADE4 (microarray ade4) was developed by Culhane et al. [ 11] for multivariate analysis of gene expression data based on the R package "ade4".
Multivariate analysis of interval censored event data based on classical likelihood methods is notoriously cumbersome.
This measure is strongly correlated with multivariate measures of body size (PC1 and centroid size) calculated from this data based on 17 landmark measurements (cf. [19]).
Although multivariate analyses could not be conducted due to the lack of information on non-infected individuals, stored data based on hospital records enabled investigation of univariate associations.
The data, based upon reported landings and auctions sales for the period 1985–2008, were analyzed using multivariate analysis.
The analysis of such multivariate data is usually based on MANOVA models assuming multivariate normality and covariance homogeneity.
For this reason we applied a suitable multivariate data analysis strategy based on projection methods which allowed us to include the correlation structure among the variables in the modeling of the response of interest.
All multivariate data analysis is therefore based on normalized data.
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