Examples of using The linear regression in English and their translations into Portuguese
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Medicine
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Ecclesiastic
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Ecclesiastic
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Computer
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Official/political
This shows that the linear regression models were adequate.
This was our objective function for the linear regression.
Introduction to the linear regression model for supervised learning.
For comparisons, the linear regression model with mixed effects was proposed.
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After this process,the following were considered for the linear regression model.
The linear regression model explained 32% of the variance of DASS.
Table 2 shows the results of the linear regression for DASS.
The linear regression model of monthly income is given by the following equation.
The linear regression model was considered appropriate when used for continuous variables.
Table 3 shows the results of the linear regression for GHS at six-month follow-up.
The linear regression result shows the contribution of age and AMC to HGS variation in women.
Regarding the"208- 0.7 x age" equation, it was generated by the linear regression of 18,712 subjects, especially between ages 20 and 70 years.
The linear regression model was used for statistical analysis, both in univariate and multivariate analysis.
Later on, the fall Constant was calculated kITT,%/min. from the linear regression of the glycemia concentrations obtained during the test.
Still, in the linear regression Table 4, it was attested that Professional Training positively impacted sleep quality.
The linear regression model explained 37.2% of the variation of the psychological domain of QOL R=.610 and R2=.372.
Cities with a positive significant variation p<0.05 in the linear regression were classified as growing epidemic, while those with a significant negative variation were decreasing epidemic.
The linear regression equations that explain the association between COP and age for each gender are shown in Table 3.
Table 4 shows the results of the linear regression carried out for each lung function value and respiratory symptom.
The linear regression between the resting energy expenditure REE and the muscular mass is presented in figure 3.
The linear regression for correlating the variables and the Analysis of Variance ANOVA were done.
The linear regression identifies an exponential relationship between the severity of adhesions and the time of dissection Figure 5.
After that, the linear regression was extrapolated in order to determine the O2 demand at the T110 velocity.
The linear regression analysis showed an association between the atheromatous plaque components and coronary artery remodelling.
Adjustments were made in the linear regression models for parasite aggregation in each parasite taxon evaluated and in the mixed-eff.