# Random variable with mean $\mu$ and variance $\sigma ^2$

I have never taken probability theory, and I wonder whether one can express some random variable $X$ with mean $\mu$ and variance $\sigma ^2$ in terms of $\mu$ and $\sigma$ only. Or at least something close to it.

-
I'd first remark that upper case Roman letters (eg. $X$) are probably a less confusing notation for random variables. Also, I don't quite understand your question; do you want $\mu$ and $\sigma$ to be the sole parameters of $x$'s distribution? – user17794 Jun 13 '12 at 23:14
You cannot express a random variable in terms of its mean and variance, but you can express its density function or distribution function etc in terms of the mean and variance. For example, the density function of a Gaussian (a.k.a. normal) random variable can be expressed in terms of its mean and variance: $f(x) = (2\pi\sigma^2)^{-1/2}\exp((x-\mu)^2/2\sigma^2)$. – Dilip Sarwate Jun 13 '12 at 23:14
@TimDuff if it is possible. Thats my question. Is it possible and if it is not then what is the closest way of doing it? – Koba Jun 13 '12 at 23:21
@DilipSarwate well. I saw somewhere that $$Z=\frac{X−μ}{σ}$$ – Koba Jun 13 '12 at 23:40
Your $Z$ will have mean $0$ and standard deviation $1$, telling you its location and scale but not its shape. It will not have a normal distribution (or any other particular shape of distribution) unless $X$ does. – Henry Jun 14 '12 at 0:08

If a random variable $X$ has mean $\mu$ and variance $\sigma^2$ then the random variable $Y$ defined by $Y=aX+b$ for real $a$ and $b$ has mean $a\mu+b$ and variance $a^2 \sigma^2$.