# Calculating variance of distribution when given various sums of that distribution

I have a distribution $X$ with an unknown mean and variance. I wish to calculate its variance, but all I am given is an infinite series of data points $(y_i, n_i)$ of the form:

$$y_i = \sum_{n_i} x$$

For each $x$ drawn independently from $X$ and $n_i$ a positive integer in some small interval, if that helps (e.g. $n_i$ can vary between 1 and 10, say).

In other words, I might be given a data point drawn from the distribution equal to drawing three samples from $X$ and summing them, the next data point might be drawn from the distribution equal to drawing eight samples from $X$ and summing them, and so on.

I already know how to estimate the mean of $X$ given any number of data points, which is just $\sum y_i / \sum n_i$ for all my data points so far (and this will become more and more accurate as I get more and more data points). But is there a formula to determine the variance of $X$ from the $(y_i, n_i)$ pairs in the same way? I've tried everything and I just can't work it out.

• 1. Should $x_i$ in the first equation be $x_j$? (Maybe something like $x_{i,j}$ would be even more clear). Are all drawn values of $x$ independent? 2. If you have an infinite series, $\sum n_i$ is infinite, so how is $\sum y_i / \sum n_i$ defined? Are you actually looking for how to estimate the mean and variance from a finite sample? – Juho Kokkala Apr 23 '18 at 6:50
• @JuhoKokkala Yes, all values of $x$ are independent. I wasn't sure how to express this in math notation; I just want to say that $y_i$ is a sum of $n_i$ values independently drawn from $X$ – hunter2 Apr 23 '18 at 6:52
• @JuhoKokkala Yes, I'd like to be able to produce an estimate after any finite number of data points, that eventually converges to the true mean/variance of $X$ as the number of data points increases. – hunter2 Apr 23 '18 at 6:55
• Could you edit the question to clarify these issues. You don't have to use "math notation" for the independence, English is fine – Juho Kokkala Apr 23 '18 at 6:55

Using the properties of variance, assuming all $x_j$ are independent, and if $Var(X) = \sigma^2$,

\begin{align*} Var(y_i) & = Var\left(\sum_{j=1}^{n_i} x_j\right)\\ & = \sum_{j=1}^{n_i} Var(x_j)\\ & = \sum_{j=1}^{n_i} \sigma^2\\ & = n_i\sigma^2\,. \end{align*}

Again, using properties of variance, this means that $$Var\left( \dfrac{y_i}{\sqrt{n_i}}\right) = \sigma^2 \,.$$

So, first, normalize all the $y_i$'s by the square root of $n_i$, that is let $$z_i = \dfrac{y_i}{\sqrt{n_i}} \,.$$

Then, each of the $z_1, z_2, ..., z_N$ have variance $\sigma^2$ and the sample mean is $$\bar{z} = \dfrac{1}{N} \sum \dfrac{y_i}{\sqrt{n_i}}\,.$$

So $Var(X)$ can be estimated with $$\hat{\sigma}^2 = \dfrac{1}{N-1} \sum_{i=1}^{N} (z_i - \bar{z})^2\,.$$

• Wow, thanks, I missing the normalization step, I was trying to divide by $n_i$ and getting confused - that fixed everything and it works beautifully! Thansk so much – hunter2 Apr 23 '18 at 8:54