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If the random variables $X_1,\ldots,X_n$ are independent and normally distributed with mean $\mu_i$ and variance $\sigma_i^2$ (i.e., standard deviation $\sigma_i$), then the sum $$ S_n = X_1 + \ldots + X_n $$ is normally distributed with mean $\mu = \mu_1 + \ldots + \mu_n$ and variance $\sigma^2 := \sigma_1^2 + \ldots + \sigma_n^2$. Thus, the average $$ ...


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Your answer is correct. The underlying theory of propogation is alluded to in the footnote of the first link you provide. Generally, if you assume that the errors in your measurements (say of, r, V, B to use your example) are unbiased with variance $\sigma_r^2,\sigma_V^2,\sigma_B^2$, respectivley, then a given function of these variables, $f(r,V,B)$, will ...



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