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 (Liu 2014 p.1). Instead, beta regression assumes the response follows a beta distribution. The beta distribution is usually given by (Ferrari 2004) \[{f(y;p,q)} = {\frac{\Gamma(p+q)}{\Gamma(p)\Gamma(q)}}y^{p-1}(1-y)^{q-1}, \quad 0<y<1\]

Where the two parameters are \(p\) and \(q\). Changing the two parameters can alter the shape of distribution drastically, given the model a lot of flexibility. It is easy to show that \[{E(y)} = \frac{p}{p+q} \equiv \mu\]

We now change the parameters from \(p\) and \(q\) to \(\mu=\frac{p}{p+q}\) and \(\phi=p+q\). This change of variables will be helpful later. The beta distribution now looks like

\[{f(y;\mu,\phi)} = {\frac{\phi}{\Gamma(\mu\phi)\Gamma((1-\mu)\phi)}}y^{\mu\phi-1}(1-y)^{q-1}, \quad 0<y<1\]

With the new parameters,

\[{E(y) = \mu}\]

and

\[{Var(y) = \frac{\mu(1-\mu)}{1+\phi}}\]

The parameter \(\phi\) is known as the precision parameter since, for fixed \(\mu\), the larger \(\phi\) the smaller the variance of y; \(\phi^{-1}\) is the dispersion parameter. The main motivation for using beta regression is that the beta distribution can take on many different shapes through the adjustment of the parameters \(\mu\) and \(\phi\). Some examples of beta distributions are shown below.

\[\beta= \begin{bmatrix} \mu_{1} \\ \mu_{2} \\ \mu_{3} \\ \end{bmatrix}\]

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