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-rw-r--r--Master/texmf-dist/doc/generic/pst-func/pst-func-doc.pdfbin3076857 -> 3076911 bytes
-rw-r--r--Master/texmf-dist/doc/generic/pst-func/pst-func-doc.tex3
2 files changed, 2 insertions, 1 deletions
diff --git a/Master/texmf-dist/doc/generic/pst-func/pst-func-doc.pdf b/Master/texmf-dist/doc/generic/pst-func/pst-func-doc.pdf
index 0b2d21c55cb..ecef832b02a 100644
--- a/Master/texmf-dist/doc/generic/pst-func/pst-func-doc.pdf
+++ b/Master/texmf-dist/doc/generic/pst-func/pst-func-doc.pdf
Binary files differ
diff --git a/Master/texmf-dist/doc/generic/pst-func/pst-func-doc.tex b/Master/texmf-dist/doc/generic/pst-func/pst-func-doc.tex
index 68dcabb7c05..8ea3d904925 100644
--- a/Master/texmf-dist/doc/generic/pst-func/pst-func-doc.tex
+++ b/Master/texmf-dist/doc/generic/pst-func/pst-func-doc.tex
@@ -54,6 +54,7 @@ Rafal Bartczuk,
Gerry Coombes,
Denis Girou,
John Frampton,
+ Leon Free,
Attila Gati,
Horst Gierhardt,
Christophe Jorssen,
@@ -1127,7 +1128,7 @@ P_p(n|N) &= \frac{N!}{n!(N-n)!}\cdot p^n(1-p)^{N-n}\label{eq:normaldistri}
Viewing the distribution as a function of the expected number of successes
%
\begin{align}\label{eq:nu}
-\lambda &= n\cdot p
+\lambda &= N\cdot p
\end{align}
%
instead of the sample size $N$ for fixed $p$, equation (2) then becomes