$\newcommand{\cov}{\operatorname{cov}}$I want to find the covariance between the sample mean and the sample variance, I think I am along the right track but am not sure.
Suppose we have i.i.d. random variables $X_1, X_2, \ldots, X_n$.
Define
$$\overline{X} = \frac{1}{n} \sum_{i=1}^n X_i \text{ and } S^2 = \frac{1}{2n(n-1)} \sum_{i=1}^n \sum_{j=1}^n (X_i - X_j)^2 $$
I want to find their covariance $\cov(\overline{X}, S^2)$. Here is what I have done:
\begin{align} & \cov(\overline{X}, S^2) = \cov\left(\frac{1}{n}\sum_{i=1}^n X_i, \frac{1}{2n(n-1)} \sum_{i=1}^n\sum_{j=1}^n (X_i - X_j)^2 \right) \\ = {} & \frac{1}{2n^2(n-1)} \sum_{i=1}^n \cov(X_i,\sum_{k=1}^{n}\sum_{j=1}^{n} (X_k - X_j)^2) \\ = {} & \frac{1}{2n^2(n-1)} \sum_{i=1}^n \cov(X_i,2\sum_{k=1}^{n} (X_i - X_k)^2) \end{align}
(because $\cov(X_i, (X_a - X_b)^2)$ is zero if $i$ isn't $a$ or $b$)
$$= \frac{1}{n^2(n-1)} \sum_{i=1}^{n} \sum_{k=1}^n \cov(X_i, (X_i - X_k)^2).$$
Now, I compute $\cov(X_i, (X_i - X_k)^2)$:
for $ i = k, \cov(X_i, (X_i - X_k)^2) = 0$
for $ i \neq k,$
\begin{align} & \cov(X_i, (X_i - X_k)^2) = \cov(X_i, X_i^2 - 2X_iX_k + X_k^2) = \cov(X_i, X_i^2) - 2\cov(X_i, X_iX_k) \\[8pt] = {} & E(X^3) - E(X)E(X^2) -2E(X)E(X^2) + 2(E(X))^3 \\[8pt] = {} & E(X^3) -3E(X)E(X^2) + 2(E(X))^3. \end{align}
When I plug this in, I do not get the answer that I want which is $E(X-E(X))^3$, can anyone please tell me where I went wrong?
UPDATE: I plugged it in again and it is actually correct! Sorry for being so careless. Thanks to anyone who has read the question. Should I delete this post?