ABSTRACT
Linear regression presents a useful opportunity to learn about both multivariate priors and full-conditional distributions as well as multivariate updates in Markov chain Monte Carlo. In this chapter, the authors describe the basic linear regression model in a number of ways. Most regression models focus on describing heterogeneity in the location of the data. Using the explicit named probability distribution as the data model reduces confusion. The data and model provide evidence that male white-tailed deer and older deer have greater body mass. The authors discuss the linear regression framework to accommodate temporal structure in data and it has been used to model continuous-time animal movement trajectories. In the Bayesian setting, the set of regression coefficients associated with the variance also need a prior.
