fixed some index entries

statistics/lecture/statistics.tex

@@ -147,11 +147,11 @@ data are smaller than the 3$^{\rm rd}$ quartile.
 %     from a normal distribution.}
 % \end{figure}
 
-\enterm{Box-whisker plots} are commonly used to visualize and compare
-the distribution of unimodal data. A box is drawn around the median
-that extends from the 1$^{\rm st}$ to the 3$^{\rm rd}$ quartile. The
-whiskers mark the minimum and maximum value of the data set
-(\figref{displayunivariatedatafig} (3)).
+\enterm[box-whisker plots]{Box-whisker plots} are commonly used to
+visualize and compare the distribution of unimodal data. A box is
+drawn around the median that extends from the 1$^{\rm st}$ to the
+3$^{\rm rd}$ quartile. The whiskers mark the minimum and maximum value
+of the data set (\figref{displayunivariatedatafig} (3)).
 
 \begin{exercise}{univariatedata.m}{}
   Generate 40 normally distributed random numbers with a mean of 2 and
@@ -175,7 +175,7 @@ The distribution of values in a data set is estimated by histograms
 
 \subsection{Histograms}
 
-\enterm[Histogram]{Histograms} count the frequency $n_i$ of
+\enterm[histogram]{Histograms} count the frequency $n_i$ of
 $N=\sum_{i=1}^M n_i$ measurements in each of $M$ bins $i$
 (\figref{diehistogramsfig} left).  The bins tile the data range
 usually into intervals of the same size. The width of the bins is
@@ -193,8 +193,9 @@ categories $i$ is the \enterm{histogram}, or the \enterm{frequency
     with the expected theoretical distribution of $P=1/6$.}
 \end{figure}
 
-Histograms are often used to estimate the \enterm{probability
-  distribution} of the data values.
+Histograms are often used to estimate the
+\enterm[probability!distribution]{probability distribution} of the
+data values.
 
 \subsection{Probabilities}
 In the frequentist interpretation of probability, the probability of
@@ -253,13 +254,14 @@ probability can also be expressed as $P(x_0<x<x_0 + \Delta x)$.
 In the limit to very small ranges $\Delta x$ the probability of
 getting a measurement between $x_0$ and $x_0+\Delta x$ scales down to
 zero with $\Delta x$:
-\[ P(x_0<x<x_0+\Delta x) \approx p(x_0) \cdot \Delta x \; . \] 
-In here the quantity $p(x_00)$ is a so called \enterm{probability
-  density} that is larger than zero and that describes the
-distribution of the data values. The probability density is not a
-unitless probability with values between 0 and 1, but a number that
-takes on any positive real number and has as a unit the inverse of the
-unit of the data values --- hence the name ``density''.
+\[ P(x_0<x<x_0+\Delta x) \approx p(x_0) \cdot \Delta x \; . \]
+In here the quantity $p(x_00)$ is a so called
+\enterm[probability!density]{probability density} that is larger than
+zero and that describes the distribution of the data values. The
+probability density is not a unitless probability with values between
+0 and 1, but a number that takes on any positive real number and has
+as a unit the inverse of the unit of the data values --- hence the
+name ``density''.
 
 \begin{figure}[t]
   \includegraphics[width=1\textwidth]{pdfprobabilities}
@@ -280,14 +282,14 @@ the probability density over the whole real axis must be one:
 \end{equation}
 
 The function $p(x)$, that assigns to every $x$ a probability density,
-is called \enterm{probability density function},
+is called \enterm[probability!density function]{probability density function},
 \enterm[pdf|see{probability density function}]{pdf}, or just
 \enterm[density|see{probability density function}]{density}
 (\determ{Wahrscheinlichkeitsdichtefunktion}). The well known
 \enterm{normal distribution} (\determ{Normalverteilung}) is an example of a
 probability density function
 \[ p_g(x) = \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}} \]
---- the \enterm{Guassian distribution}
+--- the \enterm{Gaussian distribution}
 (\determ{Gau{\ss}sche-Glockenkurve}) with mean $\mu$ and standard
 deviation $\sigma$.
 The factor in front of the exponential function ensures the normalization to