Timeline for Fitting of experimental data affected by different kinds of errors
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Dec 23, 2021 at 7:56 | comment | added | sim0 | The $\chi^2$ value can be calculated analoguously to the case of 1-d errors. See Semoi's answer below for details. | |
| Dec 23, 2021 at 7:51 | comment | added | sim0 | To account for correlations between the errors (I guess that is what you mean by "maximum errors") you will need to calculate or estimate the covariance matrix, however I do not know enough about the details of that to be of a great help here. The software kafe I mentioned before accepts these matrices as input for uncertainties so I guess there will be a way to pass them to ROOT, too. For uncorrelated errors, the covariance matrix is proportional to the identity, which is why in this cases the uncertainty can be represented as a single number, namely the proportionality constant. | |
| Dec 22, 2021 at 20:49 | answer | added | NotMe | timeline score: 1 | |
| Dec 21, 2021 at 10:01 | comment | added | Salvatore Manfredi D | Hi, but how do I rigorously compare maximum errors on the x and statistical errors on the y axis? Also ROOT can calculate fits with 2-d errors, and it also gives me the value of minimized chi-square, but where does it come from when we have 2-d errors? | |
| Dec 21, 2021 at 9:58 | history | edited | Salvatore Manfredi D | CC BY-SA 4.0 |
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| Dec 17, 2021 at 16:06 | comment | added | sim0 | If you are unsure whether or not to include some uncertainty in your data analysis, I suggest you do, to be on the safe side. There is software which can calculate fits with 2-dimensional errors, like kafe. | |
| Dec 17, 2021 at 13:22 | history | asked | Salvatore Manfredi D | CC BY-SA 4.0 |