Examples of using Multiple linear regression model in English and their translations into Portuguese
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Tables 4 and 5 show the multiple linear regression model.
A multiple linear regression model was adjusted to each of the six age groups Table 2.
Therefore, age was not used in the multiple linear regression model.
Then the multiple linear regression model was developed in two stages.
Residue analysis showed good adjustment of multiple linear regression model.
In the multiple linear regression model, age and FEV1 were collinear, the former therefore being excluded.
To analyze multiple correlations, a multiple linear regression model was used.
The multiple linear regression model included the clinical and laboratory variables with significant association with SENS and CDAI.
The evaluation of the proposed correlation is given by applying a multiple linear regression model.
Table 2 presents the final multiple linear regression model The caregiver's age only continued to adjust the model. .
Nine out of the 17 explanatory variables with p<0.20 were included in the multiple linear regression model.
They estimated the parameters by means of multiple linear regression model using the data methodology in panels.
Variables with p< 0.20 in univariate analysis were selected to be included in multiple linear regression model.
Descriptive analysis was perform and built a multiple linear regression model of the issues that most influenced the ql.
Table 5 shows that the age andrhinitis variables were significant for NIPF values in a multiple linear regression model.
Table 3 shows the multiple linear regression model for the variables HGS and flexibility/mobility, according to gender.
Data were analyzed using descriptive statistics and multiple linear regression model by ordinary least squares ols.
A multiple linear regression model was conducted from the relationship between the S/N ratio and ADAS-Cog and education variables Table 4.
From the relationship between the S/N ratio and ADAS-Cog andeducation variables, a multiple linear regression model was developed.
A multiple linear regression model was then built and variables with a significance level higher than 25% were stepwise excluded.
Variables that showed p<0.20 in the univariate analysis were inserted into the multiple linear regression model, performed for each WHOQOL-bref domain.
We subsequently applied a multiple linear regression model with twelve independent variables corresponding to the months of January to December.
For statistical treatment of the study variables, the following tests were used:Spearman's correlation coefficient, multiple linear regression model and variance analysis.
The multiple linear regression model, when investigating the relation of anxiety symptoms with sex and age, was statistically significant p 0.017.
Thanks are also due to Dr. Flávio Ziegelmann for discussions about the multiple linear regression model and to the reviewers that provided suggestions to improve the paper.
The multiple linear regression model was used to explain the relationship between the independent variables and the dependent variables of the COLLES Survey.
For the control of confounding variables in the correlation between mothers' Hb andchildren's Hb, a multiple linear regression model was adjusted with regression coefficient estimates.
Multiple linear regression model was developed, including the independent variables with significant association, assuming a normal distribution of data.
Multivariate analysis was performed using a multiple linear regression model including potential confounders variables with p value< 0.25 in the univariate analysis.
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.