Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with variance
Search options answers only
not deleted
user 92628
For questions regarding the variance of a random variable in probability, as well as the variance of a list or data in statistics.
37
votes
Accepted
Difference between Variance and 2nd moment
$$
\mathbb{E}(X^n) = \text{raw moment}\\
\mathbb{E}\left[\left(X-\mathbb{E}(X)\right)^n\right] = \text{central moment}
$$
where the 2nd central moments represents the variance. …
- 14.2k
4
votes
Generating a series with mean=0 and variance=1
Let us break this down
$$
\mathbb{E}(X) = \frac{1}{N}\sum_{k=0}^Nx_k
$$
So the mean is (relatively) easy as we just require
$$
\sum_{k=0}^Nx_k = 0
$$
Then the variance is
$$
\mathbb{V}(X) = \frac{1}{N …
- 14.2k
1
vote
Binomial variance: method proof
Part one is clear.
$$
\left(\matrix{x \\ 2}\right) = \frac{x!\cdots}{2!(x-2)!} = \frac{x(x-1)}{2} = \frac{1}{2}x^2-\frac{1}{2}x
$$
The pairs is useful relationship to get a solution for the expectatio …
- 14.2k