Exemplos de uso de Multiple regression model em Inglês e suas traduções para o Português
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A Poisson multiple regression model adjusted by sex and age.
Four variables remained associated with falls in the multiple regression model.
In the multiple regression model, log-transformed b results were expressedas 10.
The introduction of the variables into a multiple regression model was performed in steps.
At the multiple regression model, cesarean delivery remained the only independent factor for weight loss>8.
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Significance level was set at p<0.05 for the multiple regression model.
A multiple regression model was used to identify independent predictors of epicardial fat thickness.
Five variables remained associated with frailty in the multiple regression model Table 3.
The multiple regression model, including the 6MWT and gender, explained 26.6% of the total variability of SPD Table 2.
The estimated PAL value for each individual was used in the multiple regression model.
They were later analyzed in the multiple regression model, in which only variables with p.
All of the variables which had p<0.20 were included in the multiple regression model.
All women were included in the first Poisson multiple regression model, while in the second only multiparous women were.
In the present study,the intake of red meat was not statistically associated with anemia in the multiple regression model.
However, results were improved when the multiple regression model was performed. Together, the tests accounted for 9% of the pain.
Pearson's correlation coefficient was calculated and a multiple regression model was used.
The Poisson multiple regression model, which included only sociodemographic variables, revealed that the following continued to be significant.
The variable VHU1 was used as independent variable in a new multiple regression model generated by MQO.
The multiple regression model used the proportion of number of events per variable of 9:1, with confirmed balanced estimates.
However, the results have improved when the multiple regression model was performed; together the tests explained 9% of the pain.
In table 3 are presented the prevalence ratios of variables that persisted in the Poisson multiple regression model.
Poisson multiple regression model in man indicated that overall sedentarism was lower among single and separated men, students and without car in the household.
Statistical analyses were performed with the SPSS software, using the Chi-square test and Poisson multiple regression model.
In the multiple regression model, there were included the variables presenting p-value< 0.25; thus enabling a higher number of variables in the model. .
In the studies in Ribeirao Preto and Sao Luís,unknown gestational ages were imputed using a multiple regression model.
After adjusting the multiple regression model, the factors that were associated with excessive weekly weight gain were as follows: family income and pregestational nutritional status.
Prediction of independent variables was obtained by stepwise, forward, multiple regression model including potential confounders.
The multiple regression model that was used to test predictors of depression explained 18% of the variance related to depression symptoms Raj 0.11, P< 0.05; F6,69 2.57, P< 0.05.
The variables that demonstrated statistical association in the univariate regression were introduced in the multiple regression model, maintaining statistical significance.
In the present study, after adjustment in the multiple regression model, the association remained for ED 3 exclusively, in which non-white individuals had higher energy density values.