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Thus Structure appears to be identifying components of the genetic differentiation that are hard to determine by multivariate statistical approaches, but which nevertheless correspond to recognisable and coherent subsets of the gene pool.
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The validity of the findings is supported by the use of alternative and complementary multivariate statistical approaches which resulted with similar output.
Despite the fact that some collateral responses are conserved across different strains and mechanisms of resistance, our multivariate statistical approaches show overall that mechanism of resistance is the key predictor of CS and CR variability.
Multivariate statistical approaches were used to investigate the extent to which genetic (strain) background, resistance group, the putative mechanism of resistance (in particular efflux-related mutations), growth rate, and the fitness cost of resistance explain the variation in collateral responses.
The predictive modeling process involves remotely sensed imagery, Geographic Information System (GIS) data and techniques, and multivariate statistical approaches.
Here, we capitalize on multivariate statistical approaches and computational modeling to directly evaluate these theoretical perspectives.
Multivariate statistical approaches allow deriving hidden information from the dataset about the possible influences of the environment on water quality (Spanos et al. 2003).
Multivariate statistical approaches were used to reveal the relationships between soil properties, metals concentrations in soil, and their bioaccumulation in vegetables.
Multivariate statistical approaches like pattern recognition are applied in the data analysis.
To date, multivariate statistical approaches have tended to use factor and cluster analysis techniques (4).
Multivariate statistical approaches can condense the description of the relationships between multiple individuals.
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