Sentence examples for bivariate linear regression analyses from inspiring English sources

Spearman's rho test was used for bivariate linear regression analyses.

Table  2 presents the results of bivariate linear regression analyses of the relation between the QOL score and each of the variables listed in Table  1.

We then examined the association between the satisfaction subscale scores and each of the characteristics of the health workers and health facilities using bivariate linear regression analyses.

Results of a series of bivariate linear regression analyses examining the association between each independent variable and each of the eight QOL domains are presented in Table 4 (see Additional file Table 4).

Using simple (bivariate) linear regression analyses, the variables significantly associated with the mean air conduction hearing threshold (PTA, binaural average of audiometric hearing thresholds for 500 8000 Hz) were age, solvent exposure and years of factory work exposed to solvents.

The baseline cross-sectional associations between demographic, disease activity, and severity variables (gender, age, CRP, Sharp score of hands, Ritchie score, HAQ disease duration, RF IgM, anti-CCP, and use of DMARDs, steroids, or NSAIDs) and NT-proBNP levels were explored in bivariate linear regression analyses.

In bivariate weighted linear regression analyses, the most important predictors of facility CDI incidence were importation (R2=0.63, p<0.001) and antibiotic prescribing (R2=0.58, p<0.001).

Bivariate and linear regression analyses (Table 2 and Figure 2) confirmed that increases in HOMA-B and insulin levels were strongly associated with OSA severity in both men and women with NGM and for the NGM group as a whole.

Descriptive analyses, exploratory factor analyses for scale development, bivariate correlation analyses and bivariate and multiple linear regression analyses were conducted.

We used SAS version 9.2 for the univariate, bivariate, and multiple linear regression analyses and GeoDa version 0.9.8.14 for the spatial regression analyses.

Associations were investigated by bivariate and multiple linear regression analyses.