Recent content by Brais

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    Statistical uncertainty of weighted mean

    Hi again! A long time ago I had to stop this analysis and so my doubt wasn't importantr for some time :) I use the errors that my minimization algorithm "MINUIT" gives. Unfortunatelly I cannot find anything except that it (obviously) calculates a covariance matrix and error matrix... Thanks...
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    Statistical uncertainty of weighted mean

    Hi, thanks for your reply! I am calculating a fit. If I put all my data together I get an error that is higher than that of fitting different sets of points separately and then combining them with a weighted mean. I didn't do any simplification, just applied the expression seen in wikipedia...
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    Statistical uncertainty of weighted mean

    Hello! I am using physical data to do an analysis (~30k measurements). These measurements include energies, momenta, angles... of particles. I am calculating a value (call it v) at the end after a lengthy process, and if I introduce all the data into my program I did, the result is v±σ...
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    Calculate covariance matrix of two given numbers of events

    Right... after some trial and error, I got to an expression for the right solution of the covariance matrix: Given the (2x2) covariance matrix V, and the variables: N1 = a1/c N2 = -(1-c)a1/c + a2 I calculated V(m,n) = sum(i = 1 to 2) (dNm/da_i)(dNn/da_i) Error^2(Ni), where Error^2(Ni) = Ni...
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    What's the real number sign with a + under it?

    Just to mention it... normally you find it (I found it...) as a power rather than as a subscript!
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    Show the series (1/2)^sqrt(n) converges

    If I am not mistaken, it is a theorem that if the integral method works, then the sum converges in all cases, so you wouldn't need to prove the sum is smaller than the sum.
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    Calculate covariance matrix of two given numbers of events

    Hi, I am trying to follow this paper: (arXiv link). On page 18, Appendix A.1, the authors calculate a covariance matrix for two variables in a way I cannot understand. Homework Statement Variables N_1[\itex] and [itex]N_2[\itex], distributed on [itex]y \in [0, 1][\itex] as follows...
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