Examples of using Linear regression model in English and their translations into Portuguese
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Ecclesiastic
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Ecclesiastic
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Computer
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Official/political
The multiple linear regression model was used to explain the relationship between the socio-demographic variables
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.
Factors associated with QoL were identified using a multi-variable analysis of the multiple linear regression model enter method, considering a level of significance of 5% p< 0.05.
A multiple linear regression model was adjusted in order to analyze the impact of some clinical and demographic characteristics on the NIPF.
A multiple linear regression model was then built
The linear regression model with mixed effects random and fixed effects was used to achieve the objectives.
The multiple linear regression model was used to explain the relationship between the independent variables
With the help of EViews we estimated a linear regression model with the data related to the times of expansion second column, Table 3.
The residual analysis of the multiple linear regression model showed a residual dispersion suggesting a non-random distribution.
The prevalence of overweight was estimated using the simple linear regression model and the calculation of the prevalence ratio according to gender, education level.
A linear regression model with random and fixed effects was applied to compare scr and adjusted scr from the different.
Table 2 presents the final multiple linear regression model The caregiver's age only continued to adjust the model. .
Chewing time was compared between groups by a mixed effects random and fixed effects linear regression model.
Descriptive analysis was perform and built a multiple linear regression model of the issues that most influenced the ql.
The linear regression model showed that the independent variables included in the model explained the dependent variable,
including the classical linear regression model, model diagnostics,
Using a multivariate linear regression model with fixed effects,
Through the coefficients of determination(R2), the linear regression model variables, including each indicator alone,
A simple linear regression model was used to check the trends of male deaths during the study period.