Case when random variable X and its square $X^2$ are independent I have a random variable $X$, and I need to give an example when $X$ and $X^2$ are independent.
I can choose whatever distribution I please, I can say $X$ is a Bernoulli, uniform, or any other distribution.
To prove this, I need to show that $P(X \cap X^2) = P(X)P(X^2)$, but I'm failing to do so.
I tired keeping it simple, taking:
$$X \sim Bernoulli(p)$$
In this case:
$$X = X^2$$ and $p$ is probability for succeess and $1-p$ for failure: $$P(X^2) = P(X) = p$$
I also said that $$P(X \cap X^2) = P(X)P(X^2) = P(X)P(X) = p^2$$
But what is $P(X \cap X^2)$? I need to show that it is also $p^2$ in order for this to work. Is my solution enough to prove the rule of independence $P(A \cap B)=P(A)P(B)$ ?
Thank you.

A cool answer is that $X$ does not need to be degenerate. Indeed, take $X$ to have the following.
$$
P(X = 1) = p\\
P(X = -1) = 1-p\\
P(X\notin\{-1,1\}) = 0\\
p\in(0,1)
$$
Then $P(X^2 = 1)= 1$ and $P(X^2\ne 1)=0$, so, yes, $X^2$ is degenerate.
There are two cases to consider.
$1)$ $P((X=-1)\cap(X^2=1)) \overset{?}{=} P(X=-1)P(X^2=1)$
$2)$ $P((X=1)\cap(X^2=1)) \overset{?}{=} P(X=1)P(X^2=1)$
In the first case, the left side is $(1-p)\times 1=1-p$. The right side is $1-p$, so the equality holds.
In the second case, the left side if $p\times 1=p$. The right side is $p$, so the equality holds.
$\square$
ORIGINAL
To show that $X$ and $X^2$ can be independent, pick $X$ to be a degenerate Bernoulli distribution with all probability on $0$. Then $X^2$ also is a degenerate distribution with all probability on $0$. Since the sample space of each of these variables is $\{0,1\}$, there are four cases to consider.

*

*$P((X=0)\cap(X^2=0)) \overset{?}{=} P(X=0)P(X^2=0)$


*$P((X=1)\cap(X^2=0)) \overset{?}{=} P(X=1)P(X^2=0)$


*$P((X=0)\cap(X^2=1)) \overset{?}{=} P(X=0)P(X^2=1)$


*$P((X=1)\cap(X^2=1)) \overset{?}{=} P(X=1)P(X^2=1)$
In $\#1$, the right side is $1\times 1=1$. The left side refers to the probability of the event of both $X$ and $X^2$ being zero. Since both $X$ and $X^2$ are always zero, $P((X=0)\cap(X^2=0))=1$. Therefore, the equality holds.
In $\#2$, the right side is $0\times 1=0$. The left side refers to the probability that $X=1$ yet $X^2=0$, which is impossible, so $0=0$, and the equality holds.
In $\#3$, the right side if $1\times 0=0$. The left side refers to the probability that $X=0$ yet $X^2=1$, which is impossible, so $0=0$, and the equality holds.
In $\#4$, the right side is $0\times 0=0$. The left side refers to the probability that $X=1$ and $X^2=1$, which is impossible, so $0=0$, and the equality holds.
Since all four equalities hold, for all events in the sample space $\{0,1\}$, the joint probability (the $P(A\cap B)$ in the question) is equal to the product of the marginal probabilities (the $P(A)P(B)$ in the question), and independence holds.
Therefore, at least for this situation with degenerate distributions, $X$ and $X^2$ can be independent. $\square$

But, Dave, $X$ and $X^2$ are perfect predictors of each other! How could they be independent?

That's a reasonable complaint about this, and the justification comes from $X$ and $X^2$ being degenerate distributions with all probability on one value. Yes, knowing the value of either $X$ or $X^2$ allows you to know the value of the other. However, you always know the values of $X$ and $X^2$, since all probability is on one value. Therefore, knowing the value of either $X$ or $X^2$ gives you no additional information about the other, and independence is about using one variable to learn information about the other that you do not get from the marginal distribution of the other. Since the marginal distributions tell the whole story, there is no more information to be gained about the joint probability by knowing a marginal probability.
