Hi, I have to perform a regression to experimental data, which is presented in a number of vectors of dimensionality five (x1,x2,x3,x4,x5,x6). This data should be approximated by a function of the form x6=a * x1^b * x2^c * x3^d * x4^e * x5^f + g*x1^h.... and so on with parameters (a,b,c ...). The problem is, that fitting with an algorithm like finding the steepest gradient from the starting-point to minimize chi squared, is not possible, because I have no guess of appropriate starting points. I would be grateful, if someone could tell me, if ROOT was able to deal with this kind of problem und if it has advances compared with some standard software like SAS. Thanks in advance Christian Kaiser
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