A collection of gists used in blog post.

normal-distribution.tex

\begin{tikzpicture}
\begin{axis}[no markers, domain=0:10, samples=100,
axis lines*=left, xlabel=Test, ylabel=axis $y$,
height=6cm, width=10cm,
xticklabels={Test A,Test B,Test C,Test D, Test A,Test B,Test C,Test D}, ytick=\empty,
enlargelimits=false, clip=false, axis on top,
grid = major]
\addplot [fill=cyan!20, draw=none, domain=-3:3] {gauss(0,1)} \closedcycle;
\addplot [fill=orange!20, draw=none, domain=-3:-2] {gauss(0,1)} \closedcycle;
\addplot [fill=orange!20, draw=none, domain=2:3] {gauss(0,1)} \closedcycle;
\addplot [fill=blue!20, draw=none, domain=-2:-1] {gauss(0,1)} \closedcycle;
\addplot [fill=blue!20, draw=none, domain=1:2] {gauss(0,1)} \closedcycle;
\end{axis}
\end{tikzpicture}

the-motivations-behind-this-blog.md

The Normal Distribution

Also known as the Gaussian distribution, the Normal Distribution is symmetric around the mean, and has two parameters: $(\sigma,\mu)$

A given random variable $X$ is normally distributed with mean $\mu$ and standard variance $\sigma^{2}$:

$$X \sim \mathcal{N}(\mu,\sigma^{2})$$

Where:

• $\sigma$ is the standard deviation.
• $\mu$ is the mean or expected value.