Examples of using Multivariate regression model in English and their translations into Portuguese
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Table 5 shows the final multivariate regression model.
The multivariate regression model was adjusted through a stepwise method.
These coefficients of association remained similar in multivariate regression models.
The results of multivariate regression model are presented in Table 5.
However, neither maternal norfathers' educational level were retained in the final multivariate regression model.
Table 5 shows the final Poisson multivariate regression model for explaining influenza vaccine uptake.
The regressions with p-value less than 0.10 were selected and initiated in the multivariate regression model.
For equation construction, a multivariate regression model was used, and for comparison of the mean, the Student's t-test was used.
None of the variables assessed proved to be an independent predictor of all-cause mortality in the multivariate regression model.
All variables were entered simultaneously in a multivariate regression model, independent of the p-value of the crude analysis.
Multivariate regression models are a useful tool of the multivariate analysis, which takes into account the correlation between variables.
In this study, the type of school showed a significant impact on weight gain,but not in the multivariate regression model p 0.037.
An anterograde multivariate regression model was applied in order to determine which dyspnea scales and functional parameters correlated better with the 6MWD.
These data were included, together with the various dyspnea scales, in an anterograde multivariate regression model in order to determine their effect on the 6MWD.
Poisson multivariate regression models with robust variance analysis were used to test the association between receiving the BFP and the exposure and outcome variables.
Covariates independently associated with the primary endpoint in the final Cox multivariate regression model were current smoking, ADP5µM>33%, and age Table 2.
Nevertheless, association between LVDD and GDF-15 remains significant after adjustment for CAD, age, glucose metabolism andhypertension as covariates into multivariate regression models.
Associations with p< 0.20 were selected for the adjusted multivariate regression model, with a statistical significance of p< 0.05 and CI of 95.
In the multivariate regression model, mild LTPA compared with intense LTPA increased significantly the cardiometabolic risk score ß MildvsIntenseLTPA: 0.68; 95% CI: 0.18 to 1.18; pfortrend 0.007.
Glutathione and IL-6 levels were not included in the final multivariate regression model, as they did not show statistical difference in the univariate model. .
The multivariate regression model was run in the stepwise mode p> 0.1 for removal. Scores based on the addressed emotional factors were considered independent variables and MAC-Q scores were considered as dependent variables.
The variables with p-value<0.20 in the bivariate analysis were selected for the multivariate regression model, aimed to assess the impact of the explanatory variables.
The multivariate regression model identified a statistically significant association between use of water from the public network, type of house, late introduction of cow's milk, and access to health service with occurrence of diarrhea.
However, in this case, it is crucial that elaborate techniques for data analysis be used,with construction of multivariate regression models to control for potential confounding factors.
Finite mixture models and mixture multivariate regression models have been widely used for the modeling and analysis of data from a heterogeneous population.
However, in such cases,well-designed data analysis techniques are fundamentally important, with construction of multivariate regression models and controls for potential confounding factors.
The result of a multivariate regression model analysis confirms that the presence of red blood cell transfusion is an independent predictor of mortality in CABG surgery, increasing significantly the risk of death at one year OR 2.31; 95% CI 1.33-4.04; P=0.003.
Factors at home were found to be strong and significant in the associations,remaining in the final multivariate regression models and accounting for a great deal of the development variability.
In the present study were analyzed and compared the multivariate regression models for partial least squares(pls), interval partial least squares(ipls) and synergy interval partial least squares(sipls) to moisture and tannin determination in bark of acacia mearnsii.
Variables were analyzed through the Chi-square test before the logistic regression process andonly those with statistically significant differences were included in the multivariate regression model stepwise method.