Examples of using Multiple linear regression models in English and their translations into Portuguese
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Ecclesiastic
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Computer
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Multiple linear regression models were applied Y a+ b1x1+ b2x2+…+ bnxn.
Adjustment was performed using four sequential multiple linear regression models stratified by sex.
The multiple linear regression models initially included all variables with p.
The selection of independent variables for the multiple linear regression models was performed according to a stepwise model. .
In all multiple linear regression models, the use of medication was related to a lower QOL.
Thematic and correlation(LISA) maps were created for verification of spatial dependence as well as multiple linear regression models.
Multiple linear regression models estimated by the method of least squares and obtained through the stepwise method.
The power of the test, calculated a posteriori,was higher than 90% for the multiple linear regression models of saturated fat and of processed foods.
Multiple linear regression models were used considering a selected sample of size 28 based on a 7x4 factorial design.
Distribution and possible association among positive farms was evaluated by spatial autoregressive and multiple linear regression models.
We then estimated hierarchic multiple linear regression models based on the theoretical framework presented in Figure 1.
Briefly, elasticity coefficients correspond to the regression coefficients?of explanatory variables in log-log multiple linear regression models.
After adjusting the multiple linear regression models, with a 95% confidence interval, the p- values were estimated.
Briefly, elasticity coefficients correspond to the regression coefficients(β)of explanatory variables in log-log multiple linear regression models.
Multiple linear regression models with hierarchical selection of independent variables were used to evaluate the correlation between socioeconomic, environmental.
Since the cardiometabolic risk score was a continuous variable, multiple linear regression models were estimated to evaluate the effect of LTPA on cardiometabolic risk.
Multiple linear regression models were constructed to verify the influence of the carers' socio-demographic and clinical characteristics in the total score of the CBS.
For the first objective, it was used the return-based style analysis,using multiple linear regression models by the method of ordinary least squares ols.
Multiple linear regression models were constructed in order to determine the influence of the characteristics studied and the Katz index on the QoL domains.
To estimate the variables related to professional burnout, three multiple linear regression models were developed, using the reverse variable selection method.
Multiple linear regression models were used to control for the effect of gender, age, schooling, work, area of residence and number of chronic conditions.
The association between alcohol consumption andabdominal fat was assessed through multiple linear regression models adjusted for age, physical activity, smoking, and percent of body fat.
Table 4. Multiple linear regression models for the serum concentration of retinol of children assisted at Basic Health units of Goiânia, Goiás, 2013.
To estimate the effect of independent variables on the outcome of interest, multiple linear regression models with hierarchical selection of variables were used, as proposed by Victora et al.
Using multiple linear regression models, we found that low childhood sep(measured by maternal education) was associated with increased crp. however.
In the crude analysis Tables 2 and 3, only birth weight was significantly associated with weight-for-length andweight-for-age, and was the variable selected to be included in the multiple linear regression models.
We also prepared five multiple linear regression models, with B-DDS subscale scores as dependent variables, and the other investigated variables as independent ones.
Multiple linear regression models with hierarchical selection of independent variables were used to evaluate the correlation with serum retinol as the dependent variable.
The data were used to set multiple linear regression models predicting the digestible energy(de) and digestible protein(dp) contents of commercial, sampled feeds.
Multiple linear regression models were obtained and used to help explain which risk factors are associated with body weight in the sampled adolescents, thus providing a decision model. .