# Linear regression

In this course, we will use linear regression as a building block to develop more complex tool study the relationship between variables. Since its first conception in Sir Francis Galton’s work on heredity characteristics in 1886, linear regression had been extensively studied for its statistical properties and interpretation.

We will discuss these properties of linear regression in close detail, starting with model fitting and how to interpret the coefficients of the fitted model. Using the Bayesian framework, we will learn how to quantify uncertainties from the posterior distributions, and diagnose the model’s fit via plotting and simulations. We will also cover several topics in model selection, which include variable and prior selection, illustrated with coding examples in R.