# How to transform gaussian(normal) distribution to uniform distribution?

I have gaussian distributed numbers with mean 0 and variance 0.2.

And I want to transform this distribution to uniform distribution [-3 3].

How can I transform gaussian distribution numbers to uniform distribution?

A proof of line $(1)$ is as follows: Suppose $Z\sim N(0,1).$ Then for $0 \le x\le 1,$ $$\Pr(\Phi(Z) \le x) = \Pr( Z \le \Phi^{-1}(x)) = \Phi(\Phi^{-1}(x)) = x.$$
Let $X\sim \mathcal{N} (\mu, \sigma^2)$ have a normal distribution with mean $\mu=0$ and variance $\sigma^2 = 0.2$, which cumulative distribution function (CDF) is denoted by $\Phi_X$. The variable $Y = 6\Phi_X (X) - 3$ has a uniform distribution over $[{-3},3]$. In facts, \begin{aligned} \mathbb{P}(Y\leq t) &= \mathbb{P}\left(\Phi_X(X)\leq \frac{t+3}{6}\right) \\ &= \mathbb{P}\left(X\leq \Phi_X^{-1}\left(\frac{t+3}{6}\right)\right) \\ &= \Phi_X\left(\Phi_X^{-1}\left(\frac{t+3}{6}\right)\right) \\ &= \frac{t+3}{6} \, , \end{aligned} if ${-3}\leq t \leq 3$, $\mathbb{P}(Y\leq t)=0$ if $t \leq {-3}$, and $\mathbb{P}(Y\leq t)=1$ if $t \geq 3$.
• You missed something: How is the fact that $\sigma^2 =0.2$ taken into account in your answer? Commented Jul 3, 2017 at 4:58
• oh: You intended $\Phi$ to be the c.d.f. of a normal distribution other than $N(0,1).$ Nonstandard notation, but that makes your answer correct. Commented Jul 3, 2017 at 4:59