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Salvatore Manfredi D
Salvatore Manfredi D
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It is quite easy to evaluate the best-fit curve for a set of n data points when the dependent variable is affected by a statistical error (namely when you have n triplets $(x_i,y_i,\sigma_{y_i})$. I use $\chi^2 $ minimization (with ROOT software, mainly) because it also helps me evaluate the goodness of fit. But how should I behave when the $x_i$ variables are affected by a maximum uncertainty? Namely not statistical, just sensibilitysensitivity uncertainties? How do I tell if their uncertainties can be neglected? If they can't be neglected, how do I treat them?

It is quite easy to evaluate the best-fit curve for a set of n data points when the dependent variable is affected by a statistical error (namely when you have n triplets $(x_i,y_i,\sigma_{y_i})$. I use $\chi^2 $ minimization (with ROOT software, mainly) because it also helps me evaluate the goodness of fit. But how should I behave when the $x_i$ variables are affected by a maximum uncertainty? Namely not statistical, just sensibility uncertainties? How do I tell if their uncertainties can be neglected? If they can't be neglected, how do I treat them?

It is quite easy to evaluate the best-fit curve for a set of n data points when the dependent variable is affected by a statistical error (namely when you have n triplets $(x_i,y_i,\sigma_{y_i})$. I use $\chi^2 $ minimization (with ROOT software, mainly) because it also helps me evaluate the goodness of fit. But how should I behave when the $x_i$ variables are affected by a maximum uncertainty? Namely not statistical, just sensitivity uncertainties? How do I tell if their uncertainties can be neglected? If they can't be neglected, how do I treat them?

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Salvatore Manfredi D
Salvatore Manfredi D
  • 377
  • 1
  • 14

Fitting of experimental data affected by different kinds of errors

It is quite easy to evaluate the best-fit curve for a set of n data points when the dependent variable is affected by a statistical error (namely when you have n triplets $(x_i,y_i,\sigma_{y_i})$. I use $\chi^2 $ minimization (with ROOT software, mainly) because it also helps me evaluate the goodness of fit. But how should I behave when the $x_i$ variables are affected by a maximum uncertainty? Namely not statistical, just sensibility uncertainties? How do I tell if their uncertainties can be neglected? If they can't be neglected, how do I treat them?