V_r2

Quote:

There is a very good article on this topic on Wikipedia: http://en.wikipedia.org/wiki/Coefficient_of_determination

I am using the definition of r^2 given under "As explained variance". My feeling is that in the context of curve fitting, the "explained variance" interpretation is good to have. In the cases where you hold the constant term and get a V_r2 larger than 1, it is telling you that you have actually increased the variance compared to the data. Probably a good thing to know.

You will also note from that article section "As squared correlation coefficient" that the r^2 = (correlation coefficient)^2 holds only for the y = a + bx model. When you hold either a or b you are not fitting that model.

It's possible that my documentation should be expanded, with the risk of getting more complaints about the size of Igor's manuals. There is a strong inverse relationship between the length of the documentation and the probability that customers actually read it :)