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We tried to avoid overfitting by using simple regression functions with few explanatory variables.
Further simplification can be achieved by grouping the products according to their material/energy ratio and by using simple regression equations.
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The correlations between the measured variables and age were determined using simple regression analysis by the equation x = b0 + bage × age, and are presented in both graphical and table forms with significance tests for each of the equation coefficients.
Odds ratios (ORs) were determined using simple regression.
b Estimate of the regression coefficient of log-transformed for blood cadmium, lead and zinc levels by using simple linear regression.
We also analysed whether there was any correlation between age and IVCD or AAD by using simple linear regression test.
Crude ORs for continuous variables were obtained by using simple logistic regression.
Univariate analysis was performed by using simple logistic regression for association between independent and outcome variables.
Because inspection of the data suggested a relation between antimicrobial drug consumption and resistance, this assumption was formally tested by using simple linear regression.
We calculated basic frequencies, as well as odds ratios (ORs) and 95% confidence intervals (CIs) by using simple logistic regression adjusted for household design effect.
We initially found by using simple linear regression that exposures to Cd and Pb were associated, respectively, with increased risk of all types of cancer and stomach cancer among both the sexes combined and women, but that Zn exposure was not associated with the risk of site-specific cancer mortality (Table 5 and 6).
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