We present a short, inquiry-based learning course on concepts and methods underlying ordinary least squares (OLS), least absolute deviation (LAD), and quantile regression (QR). Students investigate squared, absolute, and weighted absolute distance functions (metrics) as location measures. Using differential calculus and properties of convex functions, students discover the sample mean, median, and "p"th quantile by minimizing sums of distances. Students use these metrics to define loss functions for OLS, LAD, and QR, and explore methods to minimize them. We discuss classroom experiences over two semesters. Classroom activities, Maple worksheets, and R demonstration code are available at a companion website.