diff --git a/demo_final.Rmd b/demo_final.Rmd
index 3bd0d0e5bad1527c6d5a38b1823ec35c837785f1..3560fd744be58b41af84bf787244e0a7fc6d9109 100644
--- a/demo_final.Rmd
+++ b/demo_final.Rmd
@@ -14,6 +14,38 @@ output:
knitr::opts_chunk$set(echo = TRUE)
library(sozoeko1)
```
+# Thema 1
+
+Wir wollen etwas analysieren:
+
+* x
+* y
+* z
+
+Dabei nutzen wir die Methode **OLS**.
+
+## Verwendeter Datensatz
+Wir verwenden hier die Daten vom Paket gfc.
+```{r}
+summary(gfc)
+```
+## Analyse
+Zur Analyse definieren eine neue Variable:
+```{r}
+gfc$gdpeegdpuu = gfc$GDPE/gfc$GDPU
+```
+Diese Variable ist interessant, weil:
+1. Item 1
+2. Item 2
+3. Item 3
+ + Item 3a
+ + Item 3b
+
+## Plot
+```{r}
+plot(gfc$gdpeegdpuu)
+```
+
diff --git a/demo_final.html b/demo_final.html
index 2fada5cffd3f3a11e325c8941c2656bb41d63cb1..c27d3e7dab4244996d5e7a10e96cd86eea666bb4 100644
--- a/demo_final.html
+++ b/demo_final.html
@@ -1518,7 +1518,7 @@ div.tocify {
<div id="thema-1" class="section level1" number="1">
<h1><span class="header-section-number">1</span> Thema 1</h1>
-<p>Wir wollen x y z analysieren:</p>
+<p>Wir wollen etwas analysieren:</p>
<ul>
<li>x</li>
<li>y</li>
@@ -1528,7 +1528,7 @@ div.tocify {
<div id="verwendeter-datensatz" class="section level2" number="1.1">
<h2><span class="header-section-number">1.1</span> Verwendeter
Datensatz</h2>
-<p>Datensatz abc</p>
+<p>Wir verwenden hier die Daten vom Paket gfc.</p>
<pre class="r"><code>summary(gfc)</code></pre>
<pre><code>## quarter GDPE GDPU
## Length:60 Min. : 86.70 Min. : 80.4
@@ -1540,11 +1540,15 @@ Datensatz</h2>
</div>
<div id="analyse" class="section level2" number="1.2">
<h2><span class="header-section-number">1.2</span> Analyse</h2>
-<p>Wir erstellen wir folgende neue Variable:</p>
-<pre class="r"><code>gfc$gdpegdpu <- gfc$GDPE/gfc$GDPU</code></pre>
-<p>Diese Variable ist interessant, weil: 1. a 2. b 3. und so weiter</p>
-<pre class="r"><code>plot(gfc$gdpegdpu)</code></pre>
-<p><img 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" /><!-- --></p>
+<p>Zur Analyse definieren eine neue Variable:</p>
+<pre class="r"><code>gfc$gdpeegdpuu = gfc$GDPE/gfc$GDPU</code></pre>
+<p>Diese Variable ist interessant, weil: 1. Item 1 2. Item 2 3. Item 3 +
+Item 3a + Item 3b</p>
+</div>
+<div id="plot" class="section level2" number="1.3">
+<h2><span class="header-section-number">1.3</span> Plot</h2>
+<pre class="r"><code>plot(gfc$gdpeegdpuu)</code></pre>
+<p><img 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" /><!-- --></p>
</div>
</div>
diff --git a/demo_final.md b/demo_final.md
index 26492517ec4a2a886d87a39d30942d8e9db14116..1d623d214e9f94fc691cfb7720c0e63447532e43 100644
--- a/demo_final.md
+++ b/demo_final.md
@@ -13,18 +13,16 @@ output:
# Thema 1
-Wir wollen x y z analysieren:
+Wir wollen etwas analysieren:
-* x
-* y
+* x
+* y
* z
Dabei nutzen wir die Methode **OLS**.
## Verwendeter Datensatz
-
-Datensatz abc
-
+Wir verwenden hier die Daten vom Paket gfc.
```r
summary(gfc)
@@ -39,26 +37,26 @@ summary(gfc)
## 3rd Qu.:109.47 3rd Qu.:114.9
## Max. :115.70 Max. :119.5
```
-
## Analyse
-Wir erstellen wir folgende neue Variable:
+Zur Analyse definieren eine neue Variable:
```r
-gfc$gdpegdpu <- gfc$GDPE/gfc$GDPU
+gfc$gdpeegdpuu = gfc$GDPE/gfc$GDPU
```
-
Diese Variable ist interessant, weil:
-1. a
-2. b
-3. und so weiter
+1. Item 1
+2. Item 2
+3. Item 3
+ + Item 3a
+ + Item 3b
+## Plot
```r
-plot(gfc$gdpegdpu)
+plot(gfc$gdpeegdpuu)
```
-<!-- -->
-
+<!-- -->
diff --git a/demo_final_files/figure-html/unnamed-chunk-3-1.png b/demo_final_files/figure-html/unnamed-chunk-3-1.png
new file mode 100644
index 0000000000000000000000000000000000000000..6d91df0ca0a278a2039ef2e320e7f86e9baef72a
Binary files /dev/null and b/demo_final_files/figure-html/unnamed-chunk-3-1.png differ