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In a multivariate model based on symptoms, low C4 was associated with vasculitis and pleurisy and low CH50 with vasculitis.
The developed multivariate model based on copula concept is compared with the available approaches in the literature using actual environmental data.
Chemometrics was used to develop a multivariate model based on 46 previously reported electrodialytic remediation experiments (EDR) of five different harbour sediments.
Adjusted odds ratios (OR) and 95% confidence intervals (CI) were assessed by backward selection of variables included in the multivariate model, based on the fit of the model as tested with the likelihood ratio chi-square test (p = 0.05).
Therefore, we chose the multivariate model based on PET/CT.
Variables were selected and entered into multivariate model based on results of univariate analyses (p < 0.05).
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Models were classified into three: 1 – univariate and multivariate models based on the outer dimensions of fruit. 2 – Univariate and multivariate models based on the projected areas of fruit.
In this report, we demonstrate a procedure for the construction of multivariate models based on frequency domain 2D NMR data where the loadings can be visualized as highly informative 2D loading spectra.
Covariates were included in the multivariate models based on biological and statistical considerations.
We developed multivariate models based on the identification of significant predictors of subsequent hospitalization.
A variety of multivariate models, based on measurements taken during the fasting state, have been developed.
More suggestions(13)
layered model based
varying model based
multivariate Gradient based
multivariate correlation based
multivariate method based
multivariate η2 based
multivariate analysis based
multivariate ellipse based
multivariate study based
multivariate phenotype based
multivariate copula based
multivariate regression based
multivariate model adjusted
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