robert wrote: > Is there a ready made function in numpy/scipy to compute the correlation > y=mx+o of an X and Y fast: > m, m-err, o, o-err, r-coef,r-coef-err ?
And of course, those three parameters are not particularly meaningful together. If your model is truly "y is a linear response given x with normal noise" then "y=m*x+o" is correct, and all of the information that you can get from the data will be found in the estimates of m and o and the covariance matrix of the estimates. On the other hand, if your model is that "(x, y) is distributed as a bivariate normal distribution" then "y=m*x+o" is not a particularly good representation of the model. You should instead estimate the mean vector and covariance matrix of (x, y). Your correlation coefficient will be the off-diagonal term after dividing out the marginal standard deviations. The difference between the two models is that the first places no restrictions on the distribution of x. The second does; both the x and y marginal distributions need to be normal. Under the first model, the correlation coefficient has no meaning. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
