Sentence examples for third multiple regression analysis from inspiring English sources

Third, multiple regression analysis was performed to identify the determinants of each subscale of DWC.

Third, multiple regression analysis was performed to investigate the relationship between arterial stiffness markers and clinical parameters.

A third multiple regression analysis was performed by considering 25(OH D as the dependent variable and fasting C4, BMI, insulin (or HOMAIR), and triglycerides as independent variables (fitted model: F = 5.06, P = 0.001, adjusted R = 0.213), and 25(OH D did not maintain an independent association with C4 (β = −1.108, P = 0.755).

Moreover, posture failed to remain a significant predictor of dominance after the inclusion of perceived size into a second multiple regression analysis (Table 6).

A second multiple regression analysis was performed on the subset of 45 subjects who returned for whole-body DXA scan.

In the second multiple regression analysis, we included group intervention dummies in order to test for any differences between the intervention groups.

Second, multiple regression analysis was conducted to identify the significant pain site and SIQR predictors of group membership (FM and RA/SLE).

A second multiple regression analysis was performed by considering 25(OH D as the dependent variable and fasting C3, BMI, insulin (or HOMAIR), and triglycerides as independent variables (fitted model: F = 5.73, P < 0.001, adjusted R = 0.240), and 25(OH D did not maintain an independent association with C3 (β = −2.702, P = 0.158).

To test the main hypothesis (that WM performance would predict behavioural inattention), the first multiple regression analysis had SWAN Inattention scores as the dependent variable, and examined the significance of the unique variance added to the equation by WM performance over and above that which could be accounted for by age and gender.

*p <.05; **p <.01 Next, we tested the corollary hypothesis that WM would not be associated with parent ratings of hyperactivity/impulsivity. Therefore, the second multiple regression analysis had SWAN Hyperactive/Impulsive ratings as the dependent variable to determine whether WM performance was a valid predictor over and above age and gender.

In the first multiple-regression analysis, posttest content knowledge performance was the criterion variable (Table 3).