Use Calculus to analytically derive the expression for single variable linear regression fitting parameters using the sum of square error as the loss function (show your work). That is, do the mathematical optimization problem by hand rather than with a computer Model: 1 y = M(x|p) = mx + b P = (Po, P) = (m, b) N Loss surface: L(p) = L(m, b) = ( M(, m, b)) i=1 Use matrix calculus to analytically derive the expression for two variable linear regression fitting parameters using the sum of square error as the loss function Show your work using matrix notation From your solution infer the generalized solution for an arbitrary number of variables