Examples of using Linear regression model in English and their translations into Portuguese
{-}
-
Colloquial
-
Official
-
Medicine
-
Financial
-
Ecclesiastic
-
Ecclesiastic
-
Computer
-
Official/political
Tables 4 and 5 show the multiple linear regression model.
Then the multiple linear regression model was developed in two stages.
Multivariate analysis was performed using a linear regression model.
Introduction to the linear regression model for supervised learning.
Therefore, age was not used in the multiple linear regression model.
People also translate
The linear regression model explained 32% of the variance of DASS.
After this process,the following were considered for the linear regression model.
For comparisons, the linear regression model with mixed effects was proposed.
To analyze multiple correlations,a multiple linear regression model was used.
The generalized linear regression model included all variables that achieved p.
Residue analysis showed good adjustment of multiple linear regression model.
If we have a simple linear regression model, we have some equation like Y=A1X1+A2X2. Plus.
A hypothetical association between Yi andTi can be described by the following linear regression model.
The results of the multivariate linear regression model are shown in Table 5.
The linear regression model was considered appropriate when used for continuous variables.
For statistical analysis a linear regression model or variance analysis we.
The evaluation of the proposed correlation is given by applying a multiple linear regression model.
Simple linear regression model was used to analyze and compare mortality trends.
Nine out of the 17 explanatory variables with p<0.20 were included in the multiple linear regression model.
The linear regression model explained 37.2% of the variation of the psychological domain of QOL R=.610 and R2=.372.
Variables with p< 0.20 in univariate analysis were selected to be included in multiple linear regression model.
They estimated the parameters by means of multiple linear regression model using the data methodology in panels.
The multiple linear regression model included the clinical and laboratory variables with significant association with SENS and CDAI.
Variables that showed p< 0.20 in the univariate analysis were inserted into the multiple linear regression model, performed for each WHOQOL-bref domain.
A linear regression model showed a positive association between the number of psychotropic drugs used and QTD, with r 0.341.
Table 3 shows the multiple linear regression model for the variables HGS and flexibility/mobility, according to gender.
Linear regression model was also performed between the parameters measured with two-dimensional and three-dimensional echocardiography.
Subsequently, a linear regression model was used for the right and left ears, using the absolute latency and interpeak values.
Moreover, in linear regression model is assumed that the same linear model holds for the whole data set, but this is not always valid.
The multiple linear regression model, when investigating the relation of anxiety symptoms with sex and age, was statistically significant p 0.017.