Examples of using The linear regression model in English and their translations into Portuguese
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An analysis of residues in the linear regression model was performed.
The linear regression model explained 32% of the variance of DASS.
After this process,the following were considered for the linear regression model.
Introduction to the linear regression model for supervised learning.
In six articles four cross-sectional and two cohorts,analyses were performed using the linear regression model.
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For comparisons, the linear regression model with mixed effects was proposed.
The linear regression model was considered appropriate when used for continuous variables.
Here the“best” will be understood as in the least-squares approach:such a line that minimizes the sum of squared residuals of the linear regression model.
The linear regression model explained 37.2% of the variation of the psychological domain of QOL R=.610 and R2=.372.
In a 1935 paper he introduced the concept of generalized least squares,along with now standard vector/matrix notation for the linear regression model.
The linear regression model with mixed effects random and fixed effects was used to achieve the objectives.
Adjusting the linear regression model only with the variables that are relevant to the model, it was possible to determine the following equation.
Results: The only variable associated with the duration of remission in the linear regression model was number of repetitive transcranial magnetic stimulation sessions.
In the linear regression model, however, the group variable VEs and NVEs showed no significant association p>0.05 in the set of independent variables under analysis.
Of those without it p<0. 001. The variables: nasal septum, mold, moisture, smoking,a pet in the house, were not statistically significant in the linear regression model.
First, the results from the linear regression model, which includes the set of macroeconomic variables in the variation in the CCI.
Graphs 1 and2 show the adjustment of the linear regression model to the data of latency and amplitude, respectively, with expected line for each component displayed.
The linear regression model was used in this study, adopting the increase/decrease obtained after the intervention as variable-response for each one of the variables.
To test the hypothesis of homoscedasticity of the linear regression model, we used the Levene modified test24, considering the group of children and adults.
The linear regression model, shown in Graph. 1, depicted a significant increase under the point-of-view of statistics of 2.85 ms per year of age in the age group studied.
Regarding the models' residues,spatial auto-correlation was expected in the linear regression model, as it does not presuppose the adjustment of the spatial dependence among them.
The linear regression model showed that the independent variables included in the model explained the dependent variable, implicating the assumption of the model's deterministic character.
Estimated values for a and l variables of the linear regression model as well as standard-error, and the Pearson' s correlation coefficient, are presented in Table 2.
Employing the linear regression model, the mathematical relationship between two variables was found, thus creating a practical way to estimate these animals' 24-hour PT from the results of a urine sample.
With the data handled in this way,we estimated the linear regression model for the recessionary periods in the sample, which we summarize in the following equation.
The linear regression model has the presupposition of data normality, which was verified by means of the Kolmogorov-Smirnov test for normality in the residual difference between the value used and estimated by the model after adjustment of the model. .
Through the coefficients of determination(R2), the linear regression model variables, including each indicator alone, explained approximately 30% of the total CIMT variability.