Sentence examples for bivariate linear regression analysis from inspiring English sources

Bivariate linear regression analysis was used to evaluate the relationships between serum FSTL1 levels and other parameters.

*Significant difference in change between infected and non-infected in this sub-group from bivariate linear regression analysis.

The bivariate linear regression analysis demonstrated that PD ≥ 4 mm (P = 0.008) and systolic blood pressure (P < 0.001) were significantly associated with the dependent variable "diastolic blood pressure".

In a bivariate linear regression analysis with diastolic blood pressure as dependent variable, following variables were significantly associated: PD ≥ 4 mm (P = 0.008) and systolic blood pressure (P < 0.001) (Table  3).

We employed a bivariate linear regression analysis to investigate the association between the QOL score (health index) and psychosocial factors, as well as earthquake related experiences, including earthquake related material loss and post-earthquake support.

The association between independent variables and QOL scores in each of the eight health domains was examined using bivariate linear regression analysis (Table 4; see Additional file Table 4).

Yearly trends in BMI levels were assessed by bivariate or multivariate linear regression analysis and the results were expressed as slope (95% confidence interval).

Normality with P-P plots and the Kolmogorov-Smirnov test were calculated followed by bivariate and single linear regression analysis (IBM SPPS v17, significance P <0.05).

According to both bivariate and multivariate linear regression analysis, poor sleep, living alone, current smoking, and especially poor current social support were significantly associated with long-term life dissatisfaction (logLSburden) (Table  1).

The mean change (CPET2 minus CPET1) in the curve parameters and the change in VTmax (ΔVTmax) were analysed by bivariate and multivariate linear regression analysis with age, sex, height, Δweight, ΔFEV1, ΔIRV, ΔICrest, and ∆ICdynamic as explanatory variables.

Associations between steps/day indicators and changes in anthropometric and clinical outcomes were assessed using bivariate tests and multivariable linear regression analysis which controlled for demographic and baseline covariates.