Exemplos de uso de Bivariate correlation em Inglês e suas traduções para o Português
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Bivariate correlation was used to assess the association between continuous variables.
As statistical procedures,descriptive statistics, bivariate correlation and panel modeling were used.
Bivariate correlations were used to describe relationships among constructs.
Association between covariates clinical, echocardiographic andvascular indices was assessed using the Spearman bivariate correlation.
Through the technique of bivariate correlation? Spearman, we came to the following conclusions.
Bivariate correlations of SBP and DBP with the risk variables at A2 are shown in Table 4.
To evaluate the relationship between variables, the Mann-Whitney and bivariate correlation Spearman coefficient tests were utilized.
Pearson's bivariate correlation r: to analyze the correlation between continuous variables.
Data analysis was done by spss software,which were estimated multiple bivariate correlations, pearson's correlations and anova.
The bivariate correlation showed that higher MLHF scores had worse results at the spirometry.
The main variables were also compared by Pearson bivariate correlation and multivariate analysis by ANOVA for repeated measures.
A bivariate correlation was used to compare the total score of the DERS and the positive symptoms index PSI of the BSI.
According to, p. 466, path analysis"is an approach that employs simple bivariate correlations to estimate relationships in a SEM model" in a simultaneous manner.
The bivariate correlations of sleepiness dimensions with stage-specific AHIs and with ESS score are displayed in Table 2.
In order toassess the degree of association between MHR and some variables bivariate correlation strategies were employed, including point biserial and partial correlation. .
Spearman's Bivariate Correlation Test r was used to analyze the correlation of continuous variables with or without normal distribution.
In statistical analysis,the normality of data was confirmed by the Bivariate Correlation Test MatLab, version 6.1, and the existence of disparate elements outliers through Boxplots.
A bivariate correlation was performed for a first analysis of the most important association between QOL scores physical, emotional, overall dimensions and global score and continuous variables.
After identification of those important bivariate correlations, we proceeded with the multiple regression analysis, excluding those what were not significant.
Bivariate correlations were also tested Pearson's correlation coefficient between the initial loss in PTA dB and improvement in PTA after OCT and ITC, and the time elapsed until starting ITC and improvement in PTA dB.
In our study,comorbidities had a significant bivariate correlation with QOL and although the multivariate model did not show statistically important significance, we could observe a tendency p=0.082518 in it.
Bivariate correlations demonstrated weak but significant associations between echocardiogram parameters such as EF, LVDD and LVSD, showing that a poor output capacity and an enlarged systolic and diastolic diameter of the left ventricle caused by overload can impair QOL.
Table 2 presents the bivariate correlations between female mortality due to aggression in the microregions of Rio Grande do Sul in the period 2003-2007 and the independent variables.
Based on an analysis of bivariate correlation, we observed that the variables SELIC and CDI, and IBOVESPA and Brazil Index IBrX 100 showed a strong and statistically significant correlation. .
Considering all bivariate correlations related to QOL variables plus practical experience-based variables, it was demonstrated that R= 0.64 and after the backward stepwise method, R= 0.60.
Analyses of bivariate correlations showed that resilience and dimensions of distributive and procedural justice had positive and moderate correlation with the perception of professional development, while informative and interactional justice were also po.
Considering the bivariate correlation and clinical experience, the multiple regression analysis was performed by choosing as explanatory variables GENDER, ETHNICITY, NYHA, EF, LVDD, LVSD, CHARLSON, FVC%PRED, FEV1%PRE, MVV%PRED, CR and SAS.
Statistical analysis was done in two parts: 1 Bivariate Correlation Analysis Pearson's Correlation with the two measurements, taken before and after the procedure: Pearson's correlation measures the association among 2 or more variables.
Bivariate Pearson correlation, independent t test and multiple regression analysis were used to evaluate the relationship between perceived stress and other variables.
Table 2 Index of correlation of Bivariate Moran's I between the incidence of visceral leishmaniasis in the periods of 2007-2009 and 2010-2012 and indicators and index of vulnerability.