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Re: statistical comparison of parameters from two applications of NonlinearModelFit
Hi All, Thanks for the great responses. :) > Now we're ready to talk about detecting a difference between b1 and > b2. The formal test of the hypothesis that there is no difference > is a student t: (b1 - b2)/Sqrt[ASE[b1]^2 + ASE[b2]^2] = -.170 . > The degrees of freedom are data dependent, but in your case the t > is so small that it will never be significant, no matter what the > degrees of freedom are. > > To achieve significance, the t would have to be at least 12 times > its current value, which means you would need 144 times as many > sample points as you currently have. (Note: I've cut a lot of > corners here. A proper analysis would be much messier but would > lead to the same conclusion -- you need a whole lot more data, > on the order of 200 times as much as you have shown us.) Instead of increasing the number of data points (which I can't really do at this point), couldn't I just say that in order for t to be "at least 12 times its current value", b2 needs to be less than or equal to some cutoff b2*? Also, I've read somewhere that there are tests other than Student's T, but I don't know if those are more or less appropriate for this particular scenario. > Finally, here's some code that should reproduce some of the > NonlinearModelFit output: Is there a reason why I shouldn't use NonlinearModelFit? Thanks!