diff --git a/bootstrap/lecture/bootstrap.tex b/bootstrap/lecture/bootstrap.tex
index f0fae62..79024fa 100644
--- a/bootstrap/lecture/bootstrap.tex
+++ b/bootstrap/lecture/bootstrap.tex
@@ -33,10 +33,11 @@ population. Rather, we draw samples (\enterm{simple random sample}
 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 true average length of
-the population (\determ{Populationsparameter}). We apply statistical
-methods to find out how precise this approximation is.
+the population (\endeterm{Populationsparameter}{population
+  parameter}). We apply statistical methods to find out how precise
+this approximation is.
 
-If we could draw a large number of \enterm{simple random samples} we
+If we could draw a large number of simple random samples we
 could calculate the statistical measure of interest for each sample
 and estimate its probability distribution using a histogram. This
 distribution is called the \enterm{sampling distribution}
@@ -69,17 +70,18 @@ error of the mean which is the standard deviation of the sampling
 distribution of average values around the true mean of the population
 (\subfigref{bootstrapsamplingdistributionfig}{b}).
 
-Alternatively, we can use ``bootstrapping'' to generate new samples
-from one set of measurements (resampling). From these bootstrapped
-samples we compute the desired statistical measure and estimate their
-distribution (\enterm{bootstrap distribution},
-\subfigref{bootstrapsamplingdistributionfig}{c}). Interestingly, this
-distribution is very similar to the sampling distribution regarding
-its width. The only difference is that the bootstrapped values are
-distributed around the measure of the original sample and not the one
-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).
+Alternatively, we can use \enterm{bootstrapping}
+(\determ{Bootstrap-Verfahren}) to generate new samples from one set of
+measurements (\endeterm{Resampling}{resampling}). From these
+bootstrapped samples we compute the desired statistical measure and
+estimate their distribution (\endeterm{Bootstrapverteilung}{bootstrap
+  distribution}, \subfigref{bootstrapsamplingdistributionfig}{c}).
+Interestingly, this distribution is very similar to the sampling
+distribution regarding its width. The only difference is that the
+bootstrapped values are distributed around the measure of the original
+sample and not the one 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 methods create bootstrapped samples from a SRS by
 resampling. The bootstrapped samples are used to estimate the sampling
@@ -93,11 +95,12 @@ data set using the \code{randi()} function.
 
 \section{Bootstrap of the standard error}
 
-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
-of the statistical population.
+Bootstrapping can be nicely illustrated at the example of the
+\enterm{standard error} of the mean (\determ{Standardfehler}). 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 of the statistical
+population.
 
 \begin{figure}[tp]
   \includegraphics[width=1\textwidth]{bootstrapsem}
@@ -135,9 +138,10 @@ distribution is the standard error of the mean.
 
 
 \section{Permutation tests}
-Statistical tests ask for the probability of a measured value
-to originate from a null hypothesis. Is this probability smaller than
-the desired significance level, the null hypothesis may be rejected.
+Statistical tests ask for the probability of a measured value to
+originate from a null hypothesis. Is this probability smaller than the
+desired \endeterm{Signifikanz}{significance level}, the
+\endeterm{Nullhypothese}{null hypothesis} may be rejected.
 
 Traditionally, such probabilities are taken from theoretical
 distributions which are based on assumptions about the data.  Thus the
@@ -161,22 +165,25 @@ while we conserve all other statistical properties of the data.
     statistically significant.}
 \end{figure}
 
-A good example for the application of a permutaion test is the
-statistical assessment of correlations. Given are measured pairs of
-data points $(x_i, y_i)$. By calculating the correlation coefficient
-we can quantify how strongly $y$ depends on $x$. The correlation
-coefficient alone, however, does not tell whether the correlation is
-significantly different from a random correlation. The null hypothesis
-for such a situation is that $y$ does not depend on $x$. In
-order to perform a permutation test, we need to destroy the
-correlation by permuting the $(x_i, y_i)$ pairs, i.e. we rearrange the
-$x_i$ and $y_i$ values in a random fashion. Generating many sets of
-random pairs and computing the resulting correlation coefficients
-yields a distribution of correlation coefficients that result
-randomly from uncorrelated data. By comparing the actually measured
-correlation coefficient with this distribution we can directly assess
-the significance of the correlation
-(figure\,\ref{permutecorrelationfig}).
+A good example for the application of a
+\endeterm{Permutationstest}{permutaion test} is the statistical
+assessment of \endeterm[correlation]{Korrelation}{correlations}. Given
+are measured pairs of data points $(x_i, y_i)$. By calculating the
+\endeterm[correlation!correlation
+coefficient]{Korrelation!Korrelationskoeffizient}{correlation
+  coefficient} we can quantify how strongly $y$ depends on $x$. The
+correlation coefficient alone, however, does not tell whether the
+correlation is significantly different from a random correlation. The
+\endeterm[]{Nullhypothese}{null hypothesis} for such a situation is that
+$y$ does not depend on $x$. In order to perform a permutation test, we
+need to destroy the correlation by permuting the $(x_i, y_i)$ pairs,
+i.e. we rearrange the $x_i$ and $y_i$ values in a random
+fashion. Generating many sets of random pairs and computing the
+resulting correlation coefficients yields a distribution of
+correlation coefficients that result randomly from uncorrelated
+data. By comparing the actually measured correlation coefficient with
+this distribution we can directly assess the significance of the
+correlation (figure\,\ref{permutecorrelationfig}).
 
 \begin{exercise}{correlationsignificance.m}{correlationsignificance.out}
 Estimate the statistical significance of a correlation coefficient.
diff --git a/header.tex b/header.tex
index b83bb39..0b4d53c 100644
--- a/header.tex
+++ b/header.tex
@@ -212,9 +212,19 @@
 
 %%%%% english, german, code and file terms: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \usepackage{ifthen}
+
+% \enterm[english index entry]{<english term>}
 \newcommand{\enterm}[2][]{\tr{\textit{#2}}{``#2''}\ifthenelse{\equal{#1}{}}{\tr{\protect\sindex[term]{#2}}{\protect\sindex[enterm]{#2}}}{\tr{\protect\sindex[term]{#1}}{\protect\sindex[enterm]{#1}}}}
+
+% \endeterm[english index entry]{<german index entry>}{<english term>}
+\newcommand{\endeterm}[3][]{\tr{\textit{#3}}{``#3''}\ifthenelse{\equal{#1}{}}{\tr{\protect\sindex[term]{#3}}{\protect\sindex[enterm]{#3}}}{\tr{\protect\sindex[term]{#1}}{\protect\sindex[enterm]{#1}}}\protect\sindex[determ]{#2}}
+
+% \determ[index entry]{<german term>}
 \newcommand{\determ}[2][]{\tr{``#2''}{\textit{#2}}\ifthenelse{\equal{#1}{}}{\tr{\protect\sindex[determ]{#2}}{\protect\sindex[term]{#2}}}{\tr{\protect\sindex[determ]{#1}}{\protect\sindex[term]{#1}}}}
+
+% \codeterm[index entry]{<code>}
 \newcommand{\codeterm}[2][]{\textit{#2}\ifthenelse{\equal{#1}{}}{\protect\sindex[term]{#2}}{\protect\sindex[term]{#1}}}
+
 \newcommand{\file}[1]{\texttt{#1}}
 
 % for escaping special characters into the index:
diff --git a/statistics/lecture/statistics.tex b/statistics/lecture/statistics.tex
index 3f59d46..6a6c0b4 100644
--- a/statistics/lecture/statistics.tex
+++ b/statistics/lecture/statistics.tex
@@ -455,7 +455,7 @@ bivariate or multivariate data sets where we have pairs or tuples of
 data values (e.g. size and weight of elephants) we want to analyze
 dependencies between the variables.
 
-The \enterm{correlation coefficient}
+The \enterm[correlation!correlation coefficient]{correlation coefficient}
 \begin{equation}
   \label{correlationcoefficient}
   r_{x,y} = \frac{Cov(x,y)}{\sigma_x \sigma_y} = \frac{\langle
@@ -465,7 +465,7 @@ The \enterm{correlation coefficient}
 \end{equation}
 quantifies linear relationships between two variables
 \matlabfun{corr()}.  The correlation coefficient is the
-\determ{covariance} normalized by the standard deviations of the
+\enterm{covariance} normalized by the standard deviations of the
 single variables.  Perfectly correlated variables result in a
 correlation coefficient of $+1$, anit-correlated or negatively
 correlated data in a correlation coefficient of $-1$ and un-correlated