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The multivariate analysis also revealed the interaction between the factors in terms of their effects on the PDI; hence, compared to traditional univariate analyses, the multivariate analysis allowed us to obtain a more complete understanding of the effects of the factors scrutinized.
Instead, multivariate analysis allowed the finding of consistent variation patterns within the data set.
This multivariate analysis allowed us to nest nurses into units, hospitals and countries.
Moreover, multivariate analysis allowed us to rule out the confounding effect of a number of potentially relevant covariates (see results).
Multivariate analysis allowed us to identify a number of risk factors associated with the occurrence of psychological disorders.
Our multivariate analysis allowed us to investigate how sensitive these results were to uncertainties in our input parameters and we found that in 98.6 % of cases, the fee reimbursed would cover the actual cost incurred by the health facility.
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The results from the multivariate analysis allow three conclusions.
Multivariate analysis allows for a better understanding of the association between variables, since it minimises misinterpretations caused by the existence of confounding factors.
Comprehensive measurement and deconvolution of these potential biomarkers via multivariate analysis allows predictions of disease state and evaluation of therapeutic endpoints.
Multivariate analysis allows the changes in the local environment of all 1H within a molecule to be summarized in a way that provides an easily accessible overview and defines the different states that are sampled.
The multivariate analysis allows for assessing the effect, while controlling for other variables.
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multivariate metamodelling allowed
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