Simulate a dataset with one explanatory variable and one continuous outcome variable using (y ~ dnorm(mu, sigma); mu = beta[1] + beta[2] * X). The data loads three objects: the observed y values, a coda object containing simulated values from the posterior distribution of the intercept and slope of a linear regression, and a coda object containing simulated values from the posterior predictive distribution. The purpose of the dataset is only to show the possibilities of the ggmcmc package.