Exemplos de uso de Hierarchical multiple em Inglês e suas traduções para o Português
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Simple analysis and hierarchical multiple regression analysis.
Variables that showed association p<0.20 were tested by the simple hierarchical multiple linear regression.
A hierarchical multiple logistic regression analysis was performed.
Table 3 Predictors of happiness hierarchical multiple linear regression by blocks.
In the hierarchical multiple analysis of weight-for-height deficit, only the"weight at birth" variable was found to be an associated factor.
Table 2 Predictors of self-rated health hierarchical multiple linear regression by blocks.
Hierarchical multiple analysis of the exclusive breastfeeding model shows that exclusive breastfeeding was a protective factor against overweight and obesity.
Then, we carried out the hierarchical multiple logistic regression analysis.
Only age remained significantly associated with anemia in the first group in the hierarchical multiple regression model.
Descriptive and hierarchical multiple linear regression analyses were performed.
The results of factors associated with being overweight or obese from the hierarchical multiple regression analysis are shown in Table 4.
Hierarchical multiple regression analysis was used to determine the predictive value of the assessed variables for the HRQOL scores of the studied population.
For description we used measures of frequency andconfidence interval for analysis and used the model of hierarchical multiple logistic regression.
Hierarchical multiple analysis of breastfeeding model shows that the longer breastfeeding lasts, the better protected against overweight and obesity the child is.
The theoretical Work Ability House model was tested by a hierarchical multiple logistic regression with predicted levels of hierarchy.
However, the hierarchical multiple regression models indicated that excess weight was not the main determining factor for the negative impact on most domains of the CHQ-PF50.
Table 3: Prevalence andreason of prevalence of the self-reported diabetes, according to the interest of the analysis and hierarchical multiple regression variables, Viçosa, MG, 2009.
The hierarchical multiple regression analysis revealed that the mean healthy food choice among men increases with age and physical practice, and among women, with paid work.
One of them, performed with 125 HF patients, found that five predictors schooling, social functioning, physical symptoms, functional class, andperceived health were significant using hierarchical multiple regression, which explained 26.9% of sleep disorder variance.
The hierarchical multiple analyses showed the factors independently and positively associated to the DM as the gender, the health self-perception, history of hypertension and/or dyslipidemia, polypharmacy, and abdominal obesity.
The simple hierarchical multiple linear regression tested the following variables among men: age group, skin color, marital status, paid work, practice of physical activity, smoking and risky levels of alcohol consumption.
METHODS: Two hierarchical multiple regression models were applied to a 1,042 subject sample from the city of São Paulo, southeastern Brazil, in order to evaluate relationships between indicators and determinants for centralized obesity.
The results of the hierarchical multiple analysis are on table 3 and showed that men had 2.93 times the chance of having arterial hypertension as compared to women, and that individuals aged between 41 and 50 and above 50 had, respectively, 2.14 and 3.92 times the chance of having AH, as compared to younger subjects.
The hierarchical multiple regression analyzes performed showed that(1) the meaning and purpose at work positively and significantly predicts job satisfaction, affective organizational commitment, positive affect led to work and lack of emotional exhaustion,(2) the sense of community in the team positively and significantly predicts job satisfaction and affective or.
The results of the hierarchical multiple analysis are on table IV and show that men had 2.00 times the chance of presenting alteration in glycemia as compared to women, and that individuals aged between 41 and 50 and above 50 had respectively 3.10 and 5.43 times the chance of presenting alteration in glycemia, as compared to the younger subjects.
Using hierarchical multiple linear regression models by blocks, the study attempted to identify the predictors of self-rated health status and happiness, considering the above-mentioned set of dimensions, but adjusting the potential explanatory factors within each dimension in each model, taking into account the known theoretical background.
Table 3 presents the results of multiple hierarchical analyses.
Thus, this analysis had two stages:simple and multiple hierarchical.
Correlation, multiple hierarchical regression and logistic regression tests were used to analyze relationships among variables.
To estimate the individual influenceof the outcome-associated variables, we used a multiple hierarchical model.