If the errors in x1, x2, ..., xn are e1, e2, ..., en, respectively, then the error in f(x1, x2, ..., xn) is [itex]E(f)= \frac{\partial f}{\partial x_1}e_1+ \frac{\partial f}{\partial x_2}e_2+ \cdot\cdot\cdot\frac{\partial f}{\partial x_n}e_n[/itex], approximately. (Approximately because we are using the derivative rather than the actual difference- but that will give a good upper bound on the possible error.)
In particular, if O(x,y)= x+ y then [itex]e_o= 1(e_x)+ 1(e_y)= e_x+ e_y[/itex], if P(v, i)= vi, then [itex]e_p= i e_v+ v e_i[/itex] and if P(v, i)= v/i= vi-1, then [itex]e_R= i^{-1} e_v- vi^{-2}e_i[/itex]
Notice that
[tex]\frac{e_P}{P}= \frac{i}{vi}e_v+ \frac{v}{vi} e_i= \frac{e_v}{v}+ \frac{e_i}{i}[/tex]
and
[tex]\frac{e_R}{R}= \frac{e_v}{v}- \frac{e_i}{i}[/tex]
illustrating an old mechanics "rule of thumb": if measurements are added, their errors add and if errors are multiplied, their relative errors add.