From 479e51588bb979970302c9bdb7c452ee3015d806 Mon Sep 17 00:00:00 2001
From: Jan Benda <jan.benda@uni-tuebingen.de>
Date: Mon, 25 Nov 2019 23:18:29 +0100
Subject: [PATCH] [statistics] fixed pagebreaks

---
 statistics/lecture/statistics.tex | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/statistics/lecture/statistics.tex b/statistics/lecture/statistics.tex
index 834f700..5923d8f 100644
--- a/statistics/lecture/statistics.tex
+++ b/statistics/lecture/statistics.tex
@@ -292,7 +292,6 @@ deviation $\sigma$.
 The factor in front of the exponential function ensures the normalization to
 $\int p_g(x) \, dx = 1$, \eqnref{pdfnorm}.
 
-\newpage
 \begin{exercise}{gaussianpdf.m}{gaussianpdf.out}
   \begin{enumerate}
   \item Plot the probability density of the normal distribution $p_g(x)$.
@@ -305,7 +304,6 @@ $\int p_g(x) \, dx = 1$, \eqnref{pdfnorm}.
   \end{enumerate}
 \end{exercise}
 
-\newpage
 Histograms of real valued data depend on both the number of data
 values \emph{and} the chosen bin width. As in the example with the die
 (\figref{diehistogramsfig} left), the height of the histogram gets
@@ -349,13 +347,11 @@ probability density functions like the one of the normal distribution
     normal distributions (blue).}
 \end{figure}
 
-\newpage
 \begin{exercise}{gaussianbinsnorm.m}{}
   Normalize the histogram of the previous exercise to a probability density.
 \end{exercise}
 
 
-\newpage
 \subsection{Kernel densities}
 
 A problem of using histograms for estimating probability densities is