Examples of using Logistic regression model in English and their translations into Portuguese
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Colloquial
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Medicine
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Financial
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Ecclesiastic
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Ecclesiastic
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Computer
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Official/political
The analysis used the logistic regression model.
The logistic regression model controlled for these variables.
The results of the logistic regression model was p.
A logistic regression model was estimated, adjusted for confounding factors.
Table 3 refers to the final multiple logistic regression model.
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The multiple logistic regression model was used.
All variables were tested by simple logistic regression model.
Finally, the logistic regression model demonstrated that.
A multivariate analysis was performed using the logistic regression model.
The binomial logistic regression model showed a significant association p.
Table 3 shows the results of the logistic regression model developed.
In the logistic regression model three variables which presented p.
These situations are not captured by the binomial logistic regression model.
We used a logistic regression model to evaluate independent risk factors.
Data were analyzed by the software stata using a logistic regression model.
The logistic regression model selection does not include any interaction term Table 4.
Nevertheless, no independent variable remained in the logistic regression model.
Logistic regression model(odds ratio, or) was used to identify factors associated with low bmd.
The date were statistically analyzed, employing the logistic regression model.
A logistic regression model for positive iln was developed based on clinicopathological features.
Table 4 summarizes estimates of the logistic regression model for men and women.
A logistic regression model was developed to validate the performance of lswi indistinguishing flooded areas.
These variables were included in a multivariate logistic regression model Table 3.
The logistic regression model was used to identify the independent variables associated with the neonatal death outcome.
Table 3 shows the result of the multiple logistic regression model adjustment.
Table 3 presents the logistic regression model, identifying variables independently associated with fatigue in chronic LBP patients.
The variables are included as exposure variables in a logistic regression model with the intervention as the outcome.
The logistic regression model confirmed that the liver enzyme abnormalities were significantly associated with occupational exposure production sector, even after controlling for the effects of the covariables of medical history of hepatitis, consumption of alcoholic drinks and obesity.
Then he created the hierarchical logistic regression model, composed of four(4) levels.