I have a function V = kI
where k is some constant
I_err = 0.005 A
V_err = 0.00005 V
A fit was then made, but a problem occurs when I try to calculate the reduced chi^2.
Since the error of the dependent variable V is so small, the resultant reduced chi^2 is fairly large; with good reason because the vertical error bars clearly don't overlap with the fit data.
However, the large error in the independent variable I is large enough to compensate and so the data agrees with the fit within error. If I flip the independent and dependent variables, I get a perfect reduced chi^2. However for the purposes of the fit, it would be strange to do so.
Is there a way to include the independent variable in the calculation for reduced chi^2?
The equation I'm using right now is:
chi^2 = sum(((V_exp - V_fit) / V_err)^2)
reduced chi^2 = chi^2/((# of values in V_exp) - (# of parameters))
The Attempt at a Solution
No attempt thus far except switching independent and dependent variables. Which gives a good reduced chi-squared but is not what I want.