Weights in NonlinearRegress / NonlinearFit. Versus data errors

*To*: mathgroup at smc.vnet.net*Subject*: [mg85326] Weights in NonlinearRegress / NonlinearFit. Versus data errors*From*: "Kristof Lebecki" <lebecki-hates-SPAM at ifpan.edu.pl>*Date*: Wed, 6 Feb 2008 05:38:34 -0500 (EST)

Dear all, Introduction: it happens that we, physicists have to fit a given function y(x) to a set of experimentally derived points {x_i,y_i}, i=1..n. I use for that NonlinearRegress. It happenes, however, that the experimental points are known with different accuracy. (We call usually such inaccuracy as an error.) Assume that we have a vector of triples {x_i,y_i,dy_i}, i=1..n. Now, dy_i describes the "y-error" of every point we want to fit. My first idea was to use Weights for that. Weights defined as: {w_i}= {1/dy_i} (the smaller the error the higher the weight). But my experienced colleague pointed out that what actually is here minimized is: chi^2= \sum_i w_i*(e_i)^2. He prefers that sum to be dimensionless, thus he argues that the weights should be defined as: {w_i}= {1/(dy_i)^2} And what is your opinion? Regards, Kris