Sentence examples for multivariate models gave from inspiring English sources

Independently of the Matrix used, multivariate models gave more consistent estimates of heritability (h2) and permanent environmental effect: estimates of h2 for lnFEC varied from 0.063 ± 0.037 to 0.173 ± 0.076; estimates of h2 for FAMACHA scores ranged from 0.206 ± 0.070 to 0. 343 ± 0.111; and c) estimates of h2 for PCV ranged from 0.073 ± 0.045 to 0.142 ± 0.084.

Second, results from the series of multivariate models given in Table 6 are presented.

Younger age and increased anxiety appeared were not significantly associated with non-adherence but were considered candidate variables for the multivariate models (given their P < 0.20).

We did not include the ITBS scores or individual ITBS items in adjusted, multivariate models, given that these barriers to adherence are part of the causal pathway leading to non-adherence and do not function as confounders.

We also found that good adherence is strongly related to a lower risk of death in univariate and multivariate models, given that the adherence score was considered as continuous variable or as a binomial variable (8 10 vs 0 7 points).

It is noteworthy that both CAT item usage and absolute item difficulty were significant predictors in the multivariate models given that these variables are strongly and negatively correlated (r=−.67), indicating that items of high and low difficulty are administered less frequently by CAT.

Biomarker data were logarithmically transformed prior to inclusion in the multivariate model, given the abnormal distribution.

Despite the lack of significance in the multivariate model, given previously published data [ 4- 6], we independently tested if there was a relationship between the number of estimated texts and sleepiness, but found no such correlation (r = 0.13, p = 0.07; Spearman correlation).

Adjusting for covariates using zero-inflated multivariate regression models gave similar results.

Multivariate mixture models give the possibility to classify mastitis on the basis of more than one variable and to model overlapping groups, which may improve classification even further.

The hazard ratios in the multivariate Cox models give the risk of relapse/death adjusted for time and the other covariates, for example, lymph node status.