The relationships between body mass index (BMI), age, FFQ, and PCB concentrations (wet weight) were analyzed by the Bayesian linear regression modeling separately by gender for both parents. The PCB concentration data was assessed for normal distribution with the Shapiro–Wilkes test and was not normally or log-normally distributed but was right-skewed and showed slightly heavy tails. Due to the distribution of PCB concentration, Bayesian linear regression modeling was conducted using a No-U-Turn Sampler variant of the Hamiltonian Markov Chain Monte Carlo (Hoffman and Gelman 2014) approach that was implemented in Stan version 2.8.0 and accessed through the rstan packing (Stan Development Team 2015) of the R program (R Core Team 2014). This approach was chosen because Bayesian linear regression modeling is sufficiently tolerant to handle skewed and heavy-tailed distributions. We used the non-informative N (0, 1002) prior distribution as the prior mean PCB concentration in all models.