Exemplos de uso de Logistic regression models em Inglês e suas traduções para o Português
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Three logistic regression models were elaborated.
Table 3 shows the multiple logistic regression models.
Logistic regression models were run with all these variables.
All adjustments were the same as logistic regression models.
Logistic regression models were fitted for each region in Brazil.
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Table 4 presents the adjustment of two logistic regression models.
For the analysis, logistic regression models were adjusted separately by gender.
The multivariate analysis was performed using logistic regression models.
We used logistic regression models to explore the relationship between death and independent variables.
The quantification of association was measured using logistic regression models.
Ordinal logistic regression models have shown to be suitable for analyzing data with ordinal response.
Statistical analysis included chi-square tests and logistic regression models.
Generally speaking, ordinal logistic regression models are recommended for analyzing ordinal data.
Thumbnail Table 3 Factors associated with therapy persistence: logistic regression models.
These differences were confirmed by logistic regression models controlled for maternal education.
Logistic regression models included only independent variables not collinear to the confounding variable.
In the second analysis, the multiple and simple logistic regression models were constructed.
Logistic regression models were developed in order to check the impact of independent variables on the dependent variable.
To investigate the risk factors were used logistic regression models in single and multiple approaches.
Statistical analysis included Chi-square tests, Fisher's exact test,Mann Whitney's U and logistic regression models.
Multivariate logistic regression models were applied to the sample to identify independent mortality predictors of the scores.
This model has different intercepts and coefficients for each comparison andcan be adjusted for k binary logistic regression models.
Logistic regression models were used to explore the association of spirituality to an adequate level of adherence REMADHE>= 18 points.
Variables significant in the bivariate analyses were the first entered into the multiple logistic regression models, but all other variables were tested.
Bivariate logistic regression models were built to check the isolated association between the dependent variable and each independent variable.
It was evaluated asthma control with three different control questionnaires and logistic regression models used dichotomous dependent variable as fully controlled and partially/ not controlled.
Binary logistic regression models, which predict the presence or absence of sleep apnea, could provide immediately useful information.
Based on the initial analysis, the multiple logistic regression models multivariate analysis were adjusted considering the age quantitatively and by age group.
Ordinal logistic regression models have been applied over the last few years for analyzing data, the response or outcome of which is presented in ordered categories.
Univariate and multiple logistic regression models were utilized to analyze the association between variables of interest and the occurrence of naps.