Examples of using Multiple linear regression in English and their translations into Portuguese
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Multiple linear regression was used to detect the influence of variables over overweight and obesity.
to adjust confounding variables, multiple linear regression was employed.
operational complexity variables that influence corporate governance, Multiple Linear Regression was used.
principal component analysis with a varimax rotation and multiple linear regression.
Finally, to know the variables that are predictors of self-efficacy, a multiple linear regression method enter was conducted.
The multiple linear regression using stepwise method, and analysis of non-hierarchical clustering was applied.
these are the final results of a statistical model based on multiple linear regression.
Table 3 shows the multiple linear regression model for the variables HGS
Table 4 shows the multiple linear regression analysis with the physical HRQoL factor as the dependent variable.
Multiple linear regression is the statistical technique used to proceed the analysis of secondary data.
Stepwise multiple linear regression was used for the selection of the variables that most affected the 6MWD in the CF group.
The multiple linear regression model, when investigating the relation of anxiety symptoms with sex
The multiple linear regression model, when investigating the relation between depression symptoms with the same variables, provided a similar result to that from anxiety symptoms.
Multiple linear regression analysis was used to assess the relationship between TSH
Multivariate analysis using multiple linear regression was performed only in the group of women,
A multiple linear regression analysis was performed to identify independent predictors of the 24 hours[Cl]plasma variation.
Finally, a multiple linear regression was carried out to verify how much the variables of the study influence the subjects' body attitudes.
Multivariate analysis was performed using a multiple linear regression model including potential confounders variables with p value<
The results of the multiple linear regression analysis by gender are presented in Table 2.
For the joint analysis of risk factors, multiple linear regression was employed according to the logistic regression model.