diff --git a/semester4/ps/ps-jh/parts/02_discrete-continuous-rv/05_cont-distributions/03_normal.tex b/semester4/ps/ps-jh/parts/02_discrete-continuous-rv/05_cont-distributions/03_normal.tex
index f9c4dc8..4969e1c 100644
--- a/semester4/ps/ps-jh/parts/02_discrete-continuous-rv/05_cont-distributions/03_normal.tex
+++ b/semester4/ps/ps-jh/parts/02_discrete-continuous-rv/05_cont-distributions/03_normal.tex
@@ -3,13 +3,15 @@
 mit $\sigma$ Standardabweichung. Auch: Gauss'sche Verteilung
 
 \shortdefinition[Standardnormalverteilung] $\cX \sim \cN(0, 1)$:\\
-$f_\cX = \varphi$ und $\F_\cX = \Phi = \int_{-\8}^{x} \varphi(t) \dx t = \frac{1}{\sqrt{2\phi}} \int_{-\8}^{x} e^\frac{-t^2}{2} \dx t$
+$\Phi = \int_{-\8}^{x} \varphi(t) \dx t = \frac{1}{\sqrt{2\pi}} \int_{-\8}^{x} e^\frac{-t^2}{2} \dx t$ mit $\varphi = f_\cX$.\\
+\bi{Es gilt}: $\Phi(-t) = 1 - \Phi(t)$
 
-\shorttheorem $cX \sim \cN(\mu, \sigma^2)$, dann $\frac{\cX - \mu}{\sigma} \sim \cN(0, 1)$, also:
+\shorttheorem $\cX \sim \cN(\mu, \sigma^2)$, dann $\frac{\cX - \mu}{\sigma} \sim \cN(0, 1)$, also:
 \[
     F_\cX(x) = \P[\cX \leq x] = \P\left[ \frac{\cX - \mu}{\sigma} \leq \frac{x - \mu}{\sigma} \right] = \Phi \left( \frac{x - \mu}{\sigma} \right)
 \]
 
+\newpage
 \shortexample für Phänomene modellierbar mit Normalverteilung:
 \begin{itemize}
     \item Streuung von Messwerten um Mittelwert
diff --git a/semester4/ps/ps-jh/parts/03_expected-value/02_cont.tex b/semester4/ps/ps-jh/parts/03_expected-value/02_cont.tex
index ce2fbf6..9b68fd5 100644
--- a/semester4/ps/ps-jh/parts/03_expected-value/02_cont.tex
+++ b/semester4/ps/ps-jh/parts/03_expected-value/02_cont.tex
@@ -9,7 +9,7 @@
 \begin{itemize}
     \item $\cX \sim \cU([a, b])$, $a < b$: $\E[\cX] = \frac{a + b}{2}$
     \item $\cX \sim \text{Exp}(\lambda)$, $\lambda > 0$: $\E[\cX] = \frac{1}{\lambda}$
-    \item $\cX \sim \cU(\mu, \sigma^2)$, $z = x - \mu$, $\dx z = \dx x$: $\E[\cX] = \mu$
+    \item $\cX \sim \cN(\mu, \sigma^2)$: $\E[\cX] = \mu$
     \item $\cX \sim \text{Cauchy}(x_0, \gamma)$: Existiert nicht (Int. $\8$)\\
           $\E[\cX_+] = \E[\cX_-] = \8$, Median: $0$
 \end{itemize}
diff --git a/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf b/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf
index 1c64b96..425f9f2 100644
Binary files a/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf and b/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf differ