Sentence examples for multivariate set up from inspiring English sources

We used a backward regression approach in multivariate set up to eliminate non-significant (p > 0.05) predictors from the model.

Hemoglobin did not add significantly to the prediction of superoxide dismutase in a multivariate set up (P = 0.229).

In the stepwise regression analysis for MDA, maternal albumin was found to be the most important predictor of lipid peroxidation in a multivariate set up (r = -0.885, SE = 0.171, F (1,38) = 136.6, P < 0.0001), followed by maternal hemoglobin (r = 0.925, SE = 0.586, F (2,37) = 109.577, P < 0.0001).

Multivariate model was set up including variables with IRR change ≥15% either as overall for binary variable (i.e.: HSCT and gender) or after 10 exposure units for continuous variables (i.e.: age, CVC, timing of urinary catheterization and timing of parenteral feeding).

The multivariate model is set up with the NP-Batt score as the outcome.

The cut-off for considering a result to be statistically significant in the multivariate analysis was set up at p = 0.05.

Finally, a critical discussion, giving some practical advices and pointing out the most common issues involved in multivariate set-up of CE methods, is provided.

Geometric quantiles are an extension of univariate quantiles in a multivariate set-up that uses the geometry of multivariate data clouds.

Consequently we set up various multivariate regression models in order to control for such effects.

In particular, we set up a multivariate mixed proportional hazard rate model (MMPH) that combines information related to an individual's use of the UIS system with information on employment and unemployment dynamics.

Because socio-economic position has been shown to play a large role in disparities in health, logistic regression models were set up for multivariate analysis to evaluate the effects of covariates on the assessment of self-perceived health and the reported presence of chronic conditions.