Linear regression models are one of the workhorses of clinical data science. This chapter gets into the details behind the model output. Where do the model coefficients, standard errors, and hypothesis tests come from? What is a residual and why do we care about it? What are the rest of the diagnostics reported by R in the model summary? We examine the actual numbers behind our model from Chapter 3, as well as a larger model that predicts mortality from pollution levels in small cities. (26:44; 11 pages)