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\section*{Erlang distribution}

\EQ
f(x)={\lambda^k x^{k-1} e^{-\lambda x}\over(k-1)!}
\EN

The two tables below give the skewness and the kurtosis of
the Erlang distribution for $k=1$, 2, ..., 10 and either
$\lambda=1$ or 10.
The skewness is always negative, except for $k=1$, while
in our models it is always positive. 
The kurtosis is always positive, in agreement with
our models, but is always larger than the largest
values in our models.

\begin{verbatim}
 k   skew   kurt  for (lam=1)
 1   0.21  -0.96
 2  -1.01   1.51
 3  -0.79   1.18
 4  -0.65   0.76
 5  -0.61   0.56
 6  -0.63   0.49
 7  -0.70   0.52
 8  -0.80   0.65
 9  -0.92   0.87
10  -1.03   1.16
\end{verbatim}

\begin{verbatim}
 k   skew   kurt  for (lam=10)
lam = 10
 1   0.34  -0.80
 2  -0.72   0.36
 3  -0.77   1.09
 4  -0.62   0.76
 5  -0.53   0.56
 6  -0.47   0.44
 7  -0.42   0.36
 8  -0.39   0.31
 9  -0.36   0.27
10  -0.34   0.23
\end{verbatim}

\begin{figure}[h!]\begin{center}
\includegraphics[width=\columnwidth]{lam1}
\end{center}\caption[]{
$\lambda=1$, $k=1$, 2, ..., 10.
All curves for $k=1$ are flat for small $x$.
}\label{lam1}\end{figure}

\begin{figure}[h!]\begin{center}
\includegraphics[width=\columnwidth]{lam10}
\end{center}\caption[]{
$\lambda=10$, $k=1$, 2, ..., 10.
All curves for $k=1$ are flat for small $x$.
}\label{lam10}\end{figure}

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%\bibitem[Biskamp \& M\"uller(1999)]{BM99}
%Biskamp, D., \& M\"uller, W.-C.\yprl{1999}{83}{2195}

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