diff --git a/regression/lecture/regression.tex b/regression/lecture/regression.tex
index 4f98aa5..e63e3f9 100644
--- a/regression/lecture/regression.tex
+++ b/regression/lecture/regression.tex
@@ -7,7 +7,7 @@ with a range of input signals and then the resulting responses are
 measured. This input-output relation can be described by a model. Such
 a model can be a simple function that maps the input signals to
 corresponding responses, it can be a filter, or a system of
-differential equations. In any case, the model has same parameter that
+differential equations. In any case, the model has some parameters that
 specify how input and output signals are related.  Which combination
 of parameter values are best suited to describe the input-output
 relation? The process of finding the best parameter values is an
diff --git a/simulations/lecture/simulations.tex b/simulations/lecture/simulations.tex
index e0803cc..cdba86f 100644
--- a/simulations/lecture/simulations.tex
+++ b/simulations/lecture/simulations.tex
@@ -38,10 +38,9 @@ chapter~\ref{descriptivestatisticschapter}.
 For drawing numbers $x_i$ from a normal distribution we use the
 \code{randn()} function. This function returns normally distributed
 numbers $\xi_i$ with zero mean and unit standard deviation. For
-changing the standard deviation $\sigma$ we need to multiply the
-returned data values with the required standard deviation. For
-changing the mean we just add the desired mean $\mu$ to the random
-numbers:
+changing the standard deviation we need to multiply the returned data
+values with the required standard deviation $\sigma$. For changing the
+mean we just add the desired mean $\mu$ to the random numbers:
 \begin{equation}
   x_i = \sigma \xi_i + \mu
 \end{equation}