Sum(D(i)-M(i))^2

]]>chi2= Sum (D(i)-M(i))/var(i)

where D(i) is the observed data, M(i) is the model predicted data and we “silently” assume that D(i) is normally distributed and each i measurement is independent. We minimize this random variable when searching for the best model parameters that fit the data, but we rarely think about probabilities. However, the assumptions are not valid for many X-ray observation, as the number of the observed counts follows the Poisson distribution. Different weighting (choice of var) in this expression is used to overcome the problem when the collected data has a low number of counts. Properties of the chi2 distribution are well understood and this is why we are still using it in our analysis even in case of low counts number.

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