Hopefully, it is more clear now, otherwise keep asking and I try my best to explain it again. Since the number of values (total_n) per iteration may differ, this total_n must be included as a I want some measure of the dispersion of their standard deviations. So, for each parameter, I want to know the combined and weighted standard deviation, which is composed of all the standard deviations of the iterations for a single parameter. My described data set is just an example for illustration. weightstats import DescrStatsW calculate weighted variance DescrStatsW(values, weightsweights, ddof 1 ). Note that we can also use var to quickly calculate the weighted variance as well: from statsmodels. I run this calculation 10 times with different parameters and for each parameter I run an additional 15 iterations.įor each iteration the descriptive statistics (mean, standard deviation etc.) are calculated for the results of the computation (e.g. The weighted standard deviation turns out to be 8.57. I really appreciate some help! Thanks in advance. I am confused which approach is mathematically correct and if I only have to take the squareroot of the weighted variance. PS: I also found another formular for the weighted standard deviation, respectively the variance from wikipedia. And the formulars for weighted standard deviation and weighted arithmetic mean from here.
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