Examples of using The multivariate model in English and their translations into Portuguese
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Ecclesiastic
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Computer
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equal to 20% were included in the multivariate model.
Before determining the multivariate model, a diagnosis of multicollinearity will be conducted according to the variance inflation factor VIF.
For the multivariate model a logistic regression technique was employed,
The multivariate model presented better accuracy than isolated TDI parameters for the diagnosis of CR> 3A.
The multivariate model adjusted for ultrasonographic data of the face had the worst quality of fit r 0.25.
The multivariate model was obtained through the backward procedure with elimination of the variables with p valor> 0.05.
All variables were taken to the multivariate model kept in the value of p.
The multivariate model included the variables associated with the number of vocal symptoms at 20% significance level in the univariate analysis.
The multivariate model obtained exhibited a significant area under the ROC curve 0.75; 95% CI: 0.65-0.85.
The multivariate model included all variables associated to both outcome and exposure p.
The multivariate model results for the pain symptoms in the four assessed areas are shown in Table 4.
In both approaches, the multivariate model included variables with p<
All independent variables previously mentioned composed the multivariate model gross and only presence of crosswalk in the neighborhood showed p.
Goodness-of-fit of the multivariate model was evaluated by the Hosmer and Lemeshow test.
The multivariate model for the predisposing characteristics estimated adjusted prevalence ratios close to those resulting from the univariate analysis.
First, the multivariate model linear and logistic was considered without the pollutant,
0.20 in this step were selected in order to compose the multivariate model.
with the variables selected to make up the multivariate model.
0.20 in bivariate analysis were inserted into the multivariate model.
A clinical score ranging from 0- 6 points based on logistic regression coefficients of the multivariate model was built.