Update to reflect what I think is the calculation for $E[X_1\cdot X_2]$
Given n people, if I want to estimate how many of them are likely to have an overlapping birthday with any other person, how do I calculate the variance? So far I have $E[X]=n\cdot p$ with $n$ as the number of people and $p$ the probability of any single person having an overlapping birthday
I think $E[X_1\cdot X_2]$ is as follows:
Using total probability+the fact that $X_i$ is an indicator variable: $E[X_1\cdot X_2]=P_{X_1,X_2}(1)=P(X_1\cdot X_2 |X_1=1)+P(X_1\cdot X_2 |X_1=0)$
Now again as $X_i$ is an indicator variable:
$P(X_1\cdot X_2 |X_1=0)=0$
Now from Bayes conditioning:
$P(X_1\cdot X_2 |X_1=1)=P(X_1 $and$ X_2)\cdot P(X_2)$
This is: $P(X_1\cdot X_2 |X_1=1)\cdot (1-(\frac{364}{365})^n)$
What I'm not sure about is whether: $P(X_1 $and$ X_2)=(1-(\frac{363}{364})^{n-3}+ \frac{1}{365})$
If I'm right:$P(X_1\cdot X_2 |X_1=1)\cdot (1-(\frac{364}{365})^n)=(1-(\frac{363}{364})^{n-3}+ \frac{1}{365})\cdot (1-(\frac{364}{365})^n)=E[X_1\cdot X_2]$