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First, to evaluate the effect of size on shape differences among populations (i.e., evolutionary allometry), we studied the association between consensus Procrustes shape variables (the multivariate means of the Procrustes shape coordinates) and mean ln(CS) of each population using an OLS model and estimating the evolutionary allometric shape vector.
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Each country mean vector is assumed to be an observation from a multivariate normal distribution, with a multivariate mean of 0 and covariance matrix Σ (2), representing the between-country covariance matrix among traits.
We subsequently conducted multivariate analyses by means of logistic regression to control for confounding.
A very important application of geometric quantiles is the detection of outliers in multivariate data by means of quantile contours.
This was done by investigating the influence of 13 factors, selected from RT and PCR, in a multivariate approach by means of a well-balanced three-level screening design, derived from a three-level Plackett Burman design.
Variables with p-values <0.10 were incorporated into the multivariate model by means of stepwise selection.
The remaining bad pixels are identified during unsupervised multivariate analysis by means of Hierarchical Cluster Analysis (HCA).
Variables selected this way were incorporated into a multivariate model by means of a stepwise selection approach.
Age-adjusted, but not multivariate-adjusted, geometric means of serum bilirubin were higher in men with higher levels of leisure-time physical activity.
With additional adjustment for AST (quartile categories), the multivariate-adjusted geometric means of serum bilirubin according to alcohol consumption (never, past, <25, 25 49 and ≥ 50 g/day) were 0.51, 0.50, 0.54.
Multivariate image analysis was applied to QSAR modeling of the redox potential of quinones derivatives by means of multivariate calibration such as principal component regression (PCR) and partial least squares (PLS).
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