\documentclass{article}[14pt,a4paper]
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\pgfmathdeclarefunction{gauss}{2}{\pgfmathparse{1/(#2*sqrt(2*pi))*exp(-((x-#1)^2)/(#2^2))} }
\pgfmathdeclarefunction{gauss1}{2}{\pgfmathparse{1/(#2*sqrt(2*pi))*exp(-(((1.2*x-#1)+1)^2)/(#2^2))} }
\pgfmathdeclarefunction{gauss2}{2}{\pgfmathparse{1/(#2*sqrt(2*pi))*exp(-(((1.2*x-#1)-1)^2)/(#2^2))} }
% Start the document
\begin{document}
\pagecolor{red!20}
\begin{center}
\tdplotsetmaincoords{10}{110}
\begin{tikzpicture}[scale=1.5]

\draw [white] node at (3,1){\textbf{\textcolor{green}{\Huge{Función de Densidad Binomial}}}};
\draw [white] node at (3,-0.5)
{
\begin{minipage}{13cm}\textcolor{white}{\huge{Sea $\mathsf{X}$ la variable aleatoria que mide la cantidad de éxitos $\mathsf{x}$, con probabilidad $\mathsf{p}$, que se obtienen en $\mathsf{n}$ ensayos de Bernoulli, y recordando que $\mathsf{\binom{n}{x}=\frac{n!}{x!(n-x)!}}$, entonces:}}\\
\end{minipage};
};

\draw [white] node at (3,-2){\textbf{\huge{$\mathsf{P(X = x) = f(x)\ = \binom{n}{x}\ p^x\ (1-p)^{n-x}}$ }}};

\draw [white] node at (3,-3){\textbf{\textcolor{green}{\Huge{Función Acumulada Binomial}}}};

\draw [white] node at (3,-4){\textbf{\huge{$\mathsf{\displaystyle{P(X \leq x) = F(x)\ = \Sigma_{k=0}^{x}\ \binom{n}{k}\ p^k\ (1-p)^{n-k}}}$ }}};
\draw [white] node at (3,-6)
{
\begin{minipage}{13cm}\textcolor{white}{\huge{De acuerdo al cálculo del primer y segundo momento se obtiene que:
\begin{itemize}
\item Media: $\mathsf{\mu\ =\ np}$
\item Varianza: $\mathsf{\sigma^2\ =\ np(1-p)}$
\end{itemize}
}}
\end{minipage};
};

\end{tikzpicture}

\end{center}

\begin{tikzpicture}[scale =1,information text/.style={rounded corners=7pt,inner sep=2ex}]
\shadedraw (0,5)[xshift=1.85cm] node[left,text width=10cm, information text,scale=1,left color=red,right color=magenta] {\begin{minipage}{10cm}\bf\color{white} \tikz \draw (0,0) node[fill=black,text width=9.5cm] {\textcolor{white}{La aproximación parte de la relación binomial $\mathsf{\mu\ =\ np}$
y toma como objetivo la forma $\mathsf{p\ =\ \frac{\mu}{n}}$.}\\
\vspace{0.3cm}
\Large{Si $x_1, x_2, \cdots, x_n$ es una muestra aleatoria, en donde $x_i \sim \textit{N}(\mu, \sigma)$ para todo $i$. Entonces Y, definida mediante: $$ \displaystyle{y = \Sigma_{i=1}^n \left(\frac{x_i - \mu}{\sigma}\right)^2}$$}
posee una distribución Chi-Cuadrado con $n$ grados de libertad, en símbolos: $$Y\ \sim \ \chi^2(n)$$


\vspace{0.3cm}

};
\end{minipage}};
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%\draw [white] node at (4.3,0.3){\bf{\Large{$\mathbf{\Sigma}(\mathbf{x_k} - \overline{\mathbf{x}})^3 = 0$}}};

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%Uncomment the following two lines if you want to have a bibliography1 %\bibliographystyle{alpha} %\bibliography{document} %\begin{thebibliography}{document} %\renewcommand %\refname{BIBLIOGRAFIA} %\bibitem{Baz} \textit{Bazaraa, M.S., J.J. %Jarvis} y \textit{H.D. Sheraly}, %\textit{Programación lineal y flujo de redes}, %Segunda Edición. Limusá, México, DF. 2004. %\end{thebibliography}
\end{document}