deletions | additions       diff --git a/untitled.tex b/untitled.tex  index 3a6efa2..8270339 100644  --- a/untitled.tex  +++ b/untitled.tex 
...
\section{Overview}  Beta regression is used when the output $y$ is between 0 and 1, that is $y\in[0,1]$. Common examples include the fraction of employees participating in a company 401k plan or any response that is a percentage. Linear regression has often been applied due to its simplicity but such data violates key assumptions. First, responses bounded by 0 and 1 are not normally distributed. Second, such data is usually heteroscedastic . For example, the variance shrinks as the mean approaches the boundary point 0, 1 \cite{Lockhart_2014}. 1.  Instead, beta regression assumes the response to follow follows  a beta distribution . distribution.  The beta distribution is usually given by \begin{equation}  {f(y;p,q)} = {\frac{\Gamma(p+q)}{\Gamma(p)\Gamma(q)}}y^{p-1}(1-y)^{q-1}, \quad 0  \end{equation}