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Let$ X$ be a discrete random variable with the probability mass function given by $p_x(x)= 2^{-x}$ for $x=1,2,3,\ldots$ and $0$ otherwise.

a) Let $Y=X^2$, find the probability mass function of $Y$;

b) Let $Z=X-1$, find the probability mass function of $Z$, identify the name of the distribution of $Z$ and all its parameter values.

So the probability mass function is the density function for discrete case right? I tried to integrate the function but somehow got a weird one. Is my thinking correct? thanks for any help!

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    $\begingroup$ No integration needed, not even summation needed. The random variable $Y$ can take on the values $1$, $4$, $9$, and so on. With what proabilities? We have $\Pr(Y=k^2)=2^{-k}$. That's all there is to it. For $Z$, do the same thing. What values? What probabilities? And you wil have seen something like $Z$ before, although in most probability courses it is $X$ one sees first. $\endgroup$ Commented Aug 14, 2013 at 19:28
  • $\begingroup$ Is it a geometric distribution ($1 \over 2$)? just not sure about it... $\endgroup$
    – Roy Wang
    Commented Aug 14, 2013 at 19:37
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    $\begingroup$ There are two different definitions of geometric distribution! One is the number of failures before the first success (your $Z$) and the other the number of tosses (trials) up to and including the first success. It seems your book uses the first. $\endgroup$ Commented Aug 14, 2013 at 19:40
  • $\begingroup$ You are welcome. $\endgroup$ Commented Aug 14, 2013 at 20:20

1 Answer 1

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Hint: if $g$ is a function and $X$ has probability distribution $p_X$, then

$$E[g(X)]:=\sum_{x}p_X(x)g(x),$$

where the sum is over all $x=1,2,3,\dots$, as stated in the OP.

You can find the $g$'s which define $Y$ and $Z$ and compute the corresponding expectation values. On the distribution of $Y$; as the image of $Y$ is the set of all integers $\{x^2, x=1,2,3,\dots\}$, then $p_Y(x^2)=2^{-x}$, which follows by using the expectation value formula above.

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