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Quantile regression is a highly useful tool to delineate relationships between the outcome variable and covariates.
Thereafter, bivariate analysis was conducted using a chi-square test to assess the associations between the outcome variable and the independent variables within and across the three survey years.
With the generation-mix profiles derived as in Step 1, and the wind-solar generation predicted as in Step 2, we now carry out a regression analysis to determine the relationship between the outcome variable of interest, which is the gas price paid by GFGs, denoted as (P_{k}^{text{Gas}}), and the explanatory variables it could depend on.
Associations between the outcome variable (abstinence) and each explanatory variable were investigated using chi-square test.
Table 5 shows the bivariate analysis between the outcome variable adherence and other factors.
Simple analyses were then used to assess the association between the outcome variable and the main predictors.
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Spearman correlation coefficients were calculated to identify associations between the outcome variables (patient loss to follow-up and mortality) with healthcare provider-patient ratios by TASO service sites.
Bivariate analyses were conducted to examine the relationship between the outcome variables and the predictors.
The correlation between the outcome variables was tested with the Spearman correlation coefficient.
Explorative plots assessed linearity between the outcome variables (BMC, BMD and BA) and the covariates.
The associations between the outcome variables and sociodemographic characteristics were estimated using unconditional logistic regression.
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