# A Probabilistic View of Linear Regression

One thing that I always disliked about introductory material to linear regression is how randomness is explained. The explanations always seemed unintuitive because, as I have frequently seen it, they appear as an after thought rather than the central focus of the model. In this post, I'm going to try to take another approach to building an ordinary linear regression model starting from a probabilistic point of view (which is pretty much just a Bayesian view). After the general idea is established, I'll modify the model a bit and end up with a Poisson regression using the exact same principles showing how generalized linear models aren't any more complicated. Hopefully, this will help explain the "randomness" in linear regression in a more intuitive way.