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If all of these conditions are met, a final analysis involves a mutivariable model including both the independent variable and the mediator predicting change in the outcome variable.
To examine mediation, a final logistic regression was run with both the independent variable and the proposed mediator as predictors of the dependent variable.
This was tested by regressing the dependent variables (moderate and heavy smoking pattern) on both the independent variable (single motherhood) and psychosocial stress (mediators).
The third step is to regress the dependent variable (health outcome variables) on both the independent variable (economic variables) and the mediator (environmental variables).
Confounding may occur when there is a cause (here, Black race/ethnicity) of both the independent variable, that is, the 'exposure' (here, SCT), and the dependent variable, that is, the 'outcome' (here, ERD).
When both the independent variable as well as the dependent variable are measured with error, then the effect of the independent variable is biased, usually towards the null (underestimated) [ 19, 20].
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The effect of both the independent variables on the particle size of SLN was found to be significant.
For both the independent variables of ADM and urban-centric location, the ANOVA results were non-significant (Table 7).
The increase in particle size with increase in both the independent variables can be attributed to the increasing solid content used for the formation of SLN.
From the data of multiple regression analysis (Table 2), it was confirmed that both the independent variables have significant effect on the PDE of C-SLN (r2 = 0.99).
The effect of both the independent variables on PDE in C-SLN was observed from the factorial design (Padamwar and Pokharkar 2006).
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