Sentence examples for multivariate models predicting from inspiring English sources

Multivariate models predicting energy-adjusted intake indicated strong associations with age, ethnicity, income, day care/school attendance, Supplemental Program for Women, Infants, and Children (WIC) participation, region of residence, and female head of household's educational level.

Logistic regression modelling showed that all of these factors were highly significant in both the univariate and multivariate models predicting the planned use of adjuvant RT.

Variables that correlated at p ≤ 0.10 with physical or mental function were used in the two multivariate models predicting physical and mental function, respectively.

The results of multivariate models predicting nutrient dense and energy dense factor scores in men and women by demographic and lifestyle variables are shown in Table 3. Age was a strong linear predictor, with younger participants having on average lower nutrient dense scores and higher energy dense scores than older participants among both men and women.

In multivariate models predicting the acquisition of strains resistant to each antipseudomonal agent and multiple drugs, a cutoff of 72 hours was used to dichotomize antibiotic exposure because in previous studies it appeared to be the best time span for defining the minimal duration of exposure associated with resistance [ 39, 40].

The best multivariate models predicted 65% of variability in BIBI and 62% of NEPT (p < 0.001).

This study tested a multivariate model predicting young children's internalizing behaviors from parenting practices, parents' anxiety depression and family stressors.

Our multivariate model predicting use of antipsychotic drugs and concomitant mood stabilizers identified the diagnosis of schizoaffective schizophrenia, history of having hurt someone and high scores on the General Psychopathology subscale of the PANSS (which includes items such as "uncooperativeness," "lack of judgment and insight," "poor impulse control,") as predictors.

In our multivariate model predicting participation, similar associations as in the bivariate analysis could be observed.

The multivariate model predicting syphilis among female clinic patients (n=626) included unmarried, higher education and not living in Site D (table 4).

To build a multivariate model predicting axillary LN involvement, a logistic regression with backward procedure including intra- and peritumoural 'vascular' invasion, LVI and BVI was performed.