Examples of using Multiple regression models in English and their translations into Portuguese
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Colloquial
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Official
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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 introduction of the variables into a multiple regression model was performed in steps.
A Poisson multiple regression model adjusted by sex and age.
Four variables remained associated with falls in the multiple regression model.
Five variables remained associated with frailty in the multiple regression model Table 3.
Pearson's correlation coefficient was calculated and a multiple regression model was used.
Poisson multiple regression model in man indicated that overall sedentarism was lower among single
The multiple regression model used the proportion of number of events per variable of 9:1,
They were later analyzed in the multiple regression model, in which only variables with p.
All women were included in the first Poisson multiple regression model, while in the second only multiparous women were.
A multiple regression model was used to identify independent predictors of epicardial fat thickness.
However, results were improved when the multiple regression model was performed.
At the multiple regression model, cesarean delivery remained the only independent factor for weight loss>8.
The multiple regression model, including the 6MWT
In the present study, after adjustment in the multiple regression model, the association remained for ED 3 exclusively,
However, the results have improved when the multiple regression model was performed;
The Poisson multiple regression model, which included only sociodemographic variables, revealed that the
The variable VHU1 was used as independent variable in a new multiple regression model generated by MQO.
the intake of red meat was not statistically associated with anemia in the multiple regression model.
0.20 were included in the multiple regression model.
The variables that demonstrated statistical association in the univariate regression were introduced in the multiple regression model, maintaining statistical significance.