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Generated Sun, 02 Oct 2016 10:34:49 GMT by s_hv977 (squid/3.5.20) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.7/ Connection Number of Equivalent Events. Please try the request again. Generated Sun, 02 Oct 2016 10:34:49 GMT by s_hv977 (squid/3.5.20) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.6/ Connection

The variance var(sum_w) of sum_w (i.e. We have sum_w = 90*1 + 10*0.1 = 91 events, the statistical fluctuation is coming from sqrt(90) and sqrt(10), giving var(w_i) = 1^2 * 90 + 0.1^2 * 10 = 90.1 Only a single command in Hbook **or PAW (also from** shell!!) is needed to get weighted error handling 'correctly' (sorry to all who knew this...) - Invoke statistics BEFORE filling the The system returned: (22) Invalid argument The remote host or network may be down.

We consider a bin of a histogram with N entries of weigthed events with weigths w_i, i=1,N. The error on sum_w is then given as err(sum_w) = sqrt( sum {w_i^2} ). Generated Sun, 02 Oct 2016 10:34:49 GMT by s_hv977 (squid/3.5.20) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.10/ Connection

Please try the request again. Generated Sun, 02 Oct 2016 10:34:49 **GMT by s_hv977 (squid/3.5.20) ERROR The** requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.9/ Connection The system returned: (22) Invalid argument The remote host or network may be down. Error Histogram In Neural Network For our cos(zenit)-distribution it is about 30%.

The system returned: (22) Invalid argument The remote host or network may be down. Binomial Error Bars In the division of histograms I put the "B" option to get the binomial error.I would like to know how this binomial error is calculated when the events have a weight?Thanks,Pauline These two mistakes by chance compensate to the correct final formula (poissonian and error propagation quadratic). This is not fully correct.

The problems: (1) His "Ansatz" neglects any statistics fluctuation of the data sample. (2) He makes a numerically wrong assumption about the second term being small. Histogram Error Bars Please try the request again. making an **(implicitely normalized) density** or shape distribution. For the MC-files for the atm-nu's in the Nature paper: For 7000 events we get N_equ=2200, while the number of events in the data sample is 188.

The number of equivalent events you get with "x=hstati(id,...)" or from PAW shell by $HINFO(id,'events'). This is the case e.g. Weighted Binomial Distribution for exponential distribution with a large fraction of entries in a small number of bins. Binomial Standard Deviation Your cache administrator is webmaster.

call hfill (id,......) If you also want error bars on the plot, add "call hbarx(id)" after hidopt. Your relative error is 9.49/91 = 0.105. Recommended exercise for all who believed he was right, since the mistake in his ansatz is dangerous for similar cases. Your cache administrator is webmaster. What Is Error Histogram

Work Stuff - Amanda ...... The usage of binomial statistics means that you consider the number of trials fixed to the number of entries in the given histogram. So, the "equivalent" statistics of MC is only about 12 times the data ! Derivation of above formula is based on error propagation and intrinsic poissonian statistics only.

Moderators: cranmer, rootdev Post Reply Search Advanced search First unread post • 3 posts • Page 1 of 1 PaulineBernat Posts: 3 Joined: Sat Dec 04, 2010 19:03 binomial error with Poisson Error Bars Currently the class does not support weighted events Lorenzo Top moneta Posts: 2322 Joined: Fri Jun 03, 2005 15:38 Location: CERN Re: binomial error with weighted events Quote Unread postby moneta Find here the first discussion from August, 2nd,2000.

Booking, Plotting weighted errors in PAW. Please try the request again. Original discussion of Errors and binning of the cos(zenit)-Plot for Nature. Sumw2 Root I hope to introduce a better error calculation in the case of weights in the TEfficiency class.

Note also, that the other MC-sample used (Eva's events) contain 2100 events (weight=1.) and have the same statistical significance. An erroneous way of statistics reasoning. Please try the request again. Your cache administrator is webmaster.

Your cache administrator is webmaster. The system returned: (22) Invalid argument The remote host or network may be down. Your cache administrator is webmaster. For consideration of Poissonian statistics only, here are the cos(zenit) plots, for bin=0.025, for bin=0.050, and for bin=0.10. (The latest suggestion was using Poissonian error bars for the Nature paper.) Consequences:

adding the squares of the errors on the weighted events. The system returned: (22) Invalid argument The remote host or network may be down. Please try the request again. Your cache administrator is webmaster.

Please try the request again. You see that the formula given in ansatz must be wrong immediately from the weigth==1.

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