- #1

ssd

- 268

- 6

That is,

Y= p + q.x^a+ r.sin(c+dx) + s.d^x

What I did is to start with arbitrary values of a,b,c,d and find p,q,r,s by least squares and calculate the residual sum of squares (rss).

Now I varied 'a' and compared rss till it is minimum. Then repeated the same with others, one at a time and came back to 'a' ..and so on. This resulted in a nice fit but the rss value never seem to stabilize.. it is decreasing (but of course it is not becoming 0).

My question is when should I stop ... is there any objective method or a value of rss (or value of R^2) which can be used as cut off when I can say the fit is satisfactory?

PS: Frequency distributions cannot be formed to test goodness of fit.

Any idea is appreciated.