Exemplos de uso de Multivariate linear em Inglês e suas traduções para o Português
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Model of multivariate linear regression.
Descriptive analyzes were performed and made multivariate linear regression.
Two stepwise multivariate linear regression MIR models were created.
The three-step algorithm I described is called multivariate linear regression.
Multivariate linear regression analysis was used in order to control for confounding factors.
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The association between dietary patterns andbirth weight was assessed by the multivariate linear regression model.
The analysis we used multivariate linear regression to investigate the relationship of the quota share of the icms.
The"AG" and"late-onset neonatal sepsis" variables remained significant after multivariate linear regression both p.
Multivariate linear regression evaluated the association between risk factors and periodontal disease.
The research was exploratory in nature with the methods of descriptive statistical analysis and multivariate linear regression.
Multivariate linear regression model was used to identify factors independently associated to HRQoL.
In order toassess the relationships between dependent and independent variables, multivariate linear regression models were analyzed.
A multivariate linear regression model was adjusted to determine the relation between GFR and sodium excretion.
To assess the relations of the left ventricular mass index with the demographic andfunctional variables, multivariate linear regression was used.
Through descriptive analysis and multivariate linear regression was established behavior relative to one another.
The relationships between average incidencerate of dengue and socioeconomic variables were analyzed by applying the multivariate linear regression model with stepwise selection.
Multivariate linear regression was used to evaluate the predictors for compliance with NR-32, biosafety, and standard precautions.
Statistics and Data Science II- Estimate and interpret multivariate linear regression models, including extensions appropriate for causal inference.
Multivariate linear regression models were built to identify the best combination of predictors to estimate cardiac CTA noise.
Variables selected in univariate analyses were fed into multivariate linear regression models to verify the independence of identified associations.
Thus, multivariate linear regression analyses were conducted to estimate the unique partial relationship of each substance with the quality of life dimensions.
The sample size was determined based on the need to study 15 to 20 newborn infants per independent variable to be assessed in the multivariate linear regression or logistic regression.
We also applied the multivariate linear regression model to estimate the association between food consumption patterns and birth weight.
To assess the behavior of the group considering the ischemic conditions studied, the Analysis of Variance was used with repetitive measurements,which consists of the adjustment of a multivariate linear model.
Based on the result of multivariate linear regression, the interaction between sex and BMI was significant in men only p=0.026.
To analyze the groups' behavior considering the conditions studied, analysis of variance with repeated measures was used,which consists of adjusting a multivariate linear model from which the following hypotheses were tested.
The multivariate linear regression model also showed the greater contribution of the decision time in the composition of the arrival time to the first health service.
In order to check for groups behavior, considering the conditions studied,we applied the Variance Analysis with repeated measurements technique which consists of adjusting a multivariate linear model from which the following hypotheses were tested.
Table 2 Multivariate linear regression model of the relationship between z scores for height-for-age, weight-for-height and weight-for-age and level of food insecurity.
The co-variables were then used in the statistical adjustment of the correlations in a multivariate linear regression model rank ANCOVA with the inclusion of all the covariates that achieved maximum significance of 0.2 in the bivariate analysis.