Examples of using Multiple regression model in English and their translations into Portuguese
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it was developed multiple regression model using as independent variables spillover channels
the Poisson multiple regression model was used,
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 Poisson multiple regression model, which included only sociodemographic variables, revealed that the
These factors prevented the application of a multiple regression model to assess associated factors,
In addition, a multinomial multiple regression model was used to verify the association of independent variables and different types of headache.
initially considering all covariates with a p value< 0.10 in a multiple regression model.
using the multiple regression model.
variables with p<0.20 entered the multiple regression model in a decreasing order of statistical significance stepwise forward technique.
In this study, the multiple regression model has also shown that age group,
A Cox multiple regression model was developed in the first year after CRT to assess the independent contribution of each significant echocardiographic variable previously selected in the Cox univariate model. .
The backward method was used to build the multiple regression model. In this method,
To analyze the determinants of audit fees, a multiple regression model was applied, through which this study aims to identify how
Stage 2 proposes and analyzes a multiple regression model type y=?
In the multiple regression model,"to work in the area of transference",
A Cox multiple regression model was created for the consolidation time according to the variables that presented descriptive levels lower than 0.2 p<
To design the Poisson multiple regression model, all independent variables that had a p-value< 0.20 in the univariate analysis were included in the model, considering as significant those with a p-value< 0.05.
Through the multiple regression model generated, the authors developed a nomogram in which, from the number
extrinsic attributes of male social footwear was developed a multiple regression model with dummy variables for identification
It allows the generation of trend curves based on historical data, using a multiple regression model, considering up to 60 months of data to forecast 60 months of projection.