Sentence examples for multivariate level with from inspiring English sources

Exact(2)

These predictors were also significant at the multivariate level with the exception of item discrimination.

When tested at a multivariate level with other significant dietary correlates of ID, it remained positively and strongly associated with ID possibly indicating an independent association.

Similar(58)

The analysis was performed at both univariate and multivariate levels, with a level of significance defined at a probability value less than 0.05 (P<0.05) and 0.1 (P<0.1), respectively.

The effect of gender was highly significant at the bivariate and multivariate levels with females demonstrating greater oral health literacy on all measures.

Tolerance values of less than.1 and VIFs of greater than 10 at the multivariate level could indicate a problem with multicollinearity in the SEM analysis (Kline 2005).

On a multivariate level, the following variables were associated with unreported TB: retirement (OR: 3.04, CI 1.29-7.18), smear- negative TB ORR: 1.87, CI 1.07-3.28) and the presence of extrapulmonary TB ORR: 2.07, CI 1.05-4.09) (Tables  3 and 4).

The tolerance values ranged from.431 to.930, and the VIFs ranged from 1.075 to 2.322, indicating no problems with multicollinearity at the multivariate level.

Certain factors were significantly associated with self-protective behaviors at the multivariate level.

Religion had no association with the occurrence of female genital cutting at multivariate level.

SPM overcame both problems by conducting hypothesis testing at the (massively multivariate) vector trajectory level, with random field corrections simultaneously accounting for temporal correlation and vector covariance.

These results are consistent with the suggestion that the comprehension of spoken sentences engages multiple streams of processing, including an anterior stream that shows evidence of being most strongly engaged when data are analyzed using univariate methods, and a posterior stream or streams that can be identified more readily (at a whole-brain level) with multivariate pattern-based methods.

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