Sentence examples for multivariate model given from inspiring English sources

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).

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.

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.

Thus, a cost model combining Costa's cost model with the multivariate normal distribution model given by Yang and Hancock is developed in this study to explore the effect of correlation on the designed chart parameters.

The exact formula and coefficients of the multivariate model are given in Fig.  1.

The explained variances (adjusted r2 obtained from SPSS™), after successive addition of determinants to the final multivariate model, are given.