![]() ![]() A common misconception is that the composite’s internal dispersion is. It is, however, difficult to explain and sometimes even to interpret when presented alongside annual composite performance. how many samples in each group).Īverage = np. The asset-weighted standard deviation of portfolio returns is a popular selection among investment managers for representing internal dispersion. Values, weights - Numpy ndarrays with the same shape.Īssumes that weights contains only integers (e.g. Return the weighted average and weighted sample standard deviation. Or modifying the answer by as follows: def weighted_sample_avg_std(values, weights): Var = (lhs_numerator - rhs_numerator) / denominator Applied StatisticsĪnd Probability for Engineers, Enhanced eText. Where X is the quantity each person in group i has,Īnd n is the number of people in group i. Just in case you're interested in the relation between the standard error and the standard deviation: The standard error is (for ddof = 0) calculated as the weighted standard deviation divided by the square root of the sum of the weights minus 1 ( corresponding source for statsmodels version 0.9 on GitHub): standard_error = standard_deviation / sqrt(sum(weights) - 1)Ī follow-up to "sample" or "unbiased" standard deviation in the " frequency weights" sense since "weighted sample standard deviation python" Google search leads to this post: def frequency_sample_std_dev(X, n): std_mean the standard error of weighted mean: > weighted_stats.std_mean var the weighted variance: > weighted_stats.var std the weighted standard deviation: > weighted_stats.std You initialize the class (note that you have to pass in the correction factor, the delta degrees of freedom at this point): weighted_stats = DescrStatsW(array, weights=weights, ddof=0) There is a class in statsmodels that makes it easy to calculate weighted statistics: .Īssuming this dataset and weights: import numpy as npįrom import DescrStatsW ![]()
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