diff --git a/bootstrap/lecture/bootstrap.tex b/bootstrap/lecture/bootstrap.tex
index e4654fc..557f122 100644
--- a/bootstrap/lecture/bootstrap.tex
+++ b/bootstrap/lecture/bootstrap.tex
@@ -31,15 +31,15 @@ average length of all pickles (\figref{statisticalpopulationfig}). But
 we cannot measure the lengths of all pickles in the statistical
 population. Rather, we draw samples (simple random sample
 \enterm[SRS|see{simple random sample}]{SRS}, in German:
-\determ{Stichprobe}). We then estimate a statistical measures
-(e.g. the average length of the pickles) within in this sample and
+\determ{Stichprobe}). We then estimate a statistical measure of interest
+(e.g. the average length of the pickles) within this sample and
 hope that it is a good approximation of the unknown and immeasurable
 real average length of the statistical population (in German aka
 \determ{Populationsparameter}). We apply statistical methods to find
-out how good this approximation is.
+out how precise this approximation is.
 
 If we could draw a large number of \textit{simple random samples} we could
-estimate the statistical measure of interest for each sample and
+calculate the statistical measure of interest for each sample and
 estimate the probability distribution using a histogram. This
 distribution is called the \enterm{sampling distribution} (German:
 \determ{Stichprobenverteilung},
@@ -85,16 +85,17 @@ of the statistical population. We can use the bootstrap distribution
 to draw conclusion regarding the precision of our estimation (e.g.
 standard errors and confidence intervals).
 
-Bootstrapping method create new SRS by resampling to estimate the
-sampling distribution of a statistical measure. The bootstrapped
-samples have the same size as the original sample and are created by
-sampling with replacement, that is, each value of the original sample
-can occur once, multiple time, or not at all in a bootstrapped sample.
+Bootstrapping methods create bootstrapped samples from a SRS by
+resampling. The bootstrapped samples are used to estimate the sampling
+distribution of a statistical measure. The bootstrapped samples have
+the same size as the original sample and are created by randomly drawing with
+replacement, that is, each value of the original sample can occur
+once, multiple time, or not at all in a bootstrapped sample.
 
 
 \section{Bootstrap of the standard error}
 
-Bootstrapping can be nicely illustrated at the example the standard
+Bootstrapping can be nicely illustrated at the example of the standard
 error of the mean. The arithmetic mean is calculated for a simple
 random sample. The standard error of the mean is the standard
 deviation of the expected distribution of mean values around the mean
@@ -120,13 +121,13 @@ distribution is the standard error of the mean.
 \pagebreak[4]
 \begin{exercise}{bootstrapsem.m}{bootstrapsem.out}
   Create the distribution of mean values from bootstrapped samples
-  resampled form a single SRS. Use this distribution to estimate the
+  resampled from a single SRS. Use this distribution to estimate the
   standard error of the mean.
   \begin{enumerate}
   \item Draw 1000 normally distributed random number and calculate the
-    mean, the standard deviation and the standard error
+    mean, the standard deviation, and the standard error
     ($\sigma/\sqrt{n}$).
-  \item Resample the data 1000 times (draw and replace) and calculate
+  \item Resample the data 1000 times (randomly draw and replace) and calculate
     the mean of each bootstrapped sample.
   \item Plot a histogram of the respective distribution and calculate its mean and
     standard deviation. Compare with the
@@ -135,7 +136,7 @@ distribution is the standard error of the mean.
 \end{exercise}
 
 
-\section{Permutationtests}
+\section{Permutation tests}
 Statistical tests ask for the probability that a measured value
 originates from the null hypothesis. Is this probability smaller than
 the desired significance level, the null hypothesis may be rejected.
@@ -182,16 +183,16 @@ statistical significance (figure\,\ref{permutecorrelationfig}).
 Estimate the statistical significance of a correlation coefficient.
 \begin{enumerate}
 \item Create pairs of $(x_i, y_i)$ values. Randomly choose $x$-values
-  and calculate the respective $y$-values according to $y=0.2 \cdot x$
-  to which you add a random value drawn from a normal distribution.
+  and calculate the respective $y$-values according to $y_i =0.2 \cdot x_i + u_i$
+  where $u_i$ is a random number drawn from a normal distribution.
 \item Calculate the correlation coefficient.
 \item Generate the distribution according to the null hypothesis by
   generating uncorrelated pairs. For this permute $x$- and $y$-values
-  (\matlabfun{randperm()}) 1000 times and calculate for each
+  \matlabfun{randperm()} 1000 times and calculate for each
   permutation the correlation coefficient.
-\item From the resulting null hypothesis distribution the 95\,\%
-  percentile and compare it with the correlation coefficient
-  calculated for the original data.
+\item Read out the 95\,\% percentile from the resulting null
+  hypothesis distribution and compare it with the correlation
+  coefficient calculated for the original data.
 \end{enumerate}
 \end{exercise}
 
diff --git a/header.tex b/header.tex
index 38fe4e2..567b1fc 100644
--- a/header.tex
+++ b/header.tex
@@ -5,7 +5,7 @@
 \author{{\LARGE Jan Grewe \& Jan Benda}\\[5ex]Abteilung Neuroethologie\\[2ex]%
         \includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}\vspace{3ex}}
 
-\date{WS 2018/2019\\\vfill%
+\date{WS 2019/2020\\\vfill%
       \centerline{\includegraphics[width=0.7\textwidth]{announcements/correlationcartoon}%
       \rotatebox{90}{\footnotesize\url{www.xkcd.com}}}}