Changed comment syntax

demo_final.Rmd

@@ -26,13 +26,7 @@ Wir wollen etwas analysieren:
 * y 
 
 
------------  
-
-# TODO: Ehrlich gesagt weiß ich nicht, ob das die richtige Methode ist für # das, was wir machen sollen.
-# Lasst uns das noch einmal diskutieren.  
-
-# TODO 2 Das müssen wir auch noch machen.
---------------  
+<!-- ToDo: Wollen wir nicht lieber xyz? -->
 
 Dabei nutzen wir die Methode **OLS**.
 

demo_final.html

@@ -1520,10 +1520,13 @@ div.tocify {
 <h1><span class="header-section-number">1</span> Thema 1</h1>
 <p>Wir wollen etwas analysieren:</p>
 <ul>
+<li>Kompromiss</li>
+<li>Kompromiss</li>
+<li>f</li>
 <li>x</li>
 <li>y</li>
-<li>z</li>
 </ul>
+<!-- ToDo: Wollen wir nicht lieber xyz? -->
 <p>Dabei nutzen wir die Methode <strong>OLS</strong>.</p>
 <div id="verwendeter-datensatz" class="section level2" number="1.1">
 <h2><span class="header-section-number">1.1</span> Verwendeter
@@ -1542,8 +1545,6 @@ Datensatz</h2>
 <h2><span class="header-section-number">1.2</span> Analyse</h2>
 <p>Zur Analyse definieren eine neue Variable:</p>
 <pre class="r"><code>gfc$gdpeegdpuu = gfc$GDPE/gfc$GDPU</code></pre>
-<p>Diese Variable ist interessant, weil: 1. Item 1<br />
-2. Item 2 3. Item 3 + Item 3a + Item 3b</p>
 <p>Diese Variable ist interessant, weil:</p>
 <ol style="list-style-type: decimal">
 <li>Item 1<br />
@@ -1561,6 +1562,11 @@ Datensatz</h2>
 <pre class="r"><code>plot(gfc$gdpeegdpuu)</code></pre>
 <p><img src="data:image/png;base64,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" /><!-- --></p>
 </div>
+<div id="interpretation" class="section level2" number="1.4">
+<h2><span class="header-section-number">1.4</span> Interpretation</h2>
+<p>Aus der Analyse können wir ableiten, dass der Trend nach unten geht.
+Dies zeigt uns auch der Plot. Die ökonomischen Implikationen sind:</p>
+</div>
 </div>

demo_final.md

@@ -15,9 +15,15 @@ output:
 
 Wir wollen etwas analysieren:
 
+
+* Kompromiss 
+* Kompromiss
+* f
 * x
 * y 
-* z
+
+
+<!-- ToDo: Wollen wir nicht lieber xyz? -->
 
 Dabei nutzen wir die Methode **OLS**.
 
@@ -38,17 +44,11 @@ summary(gfc)
 ##                     Max.   :115.70   Max.   :119.5
 ```
 ## Analyse
-Zur Analyse definieren wir eine neue Variable:
+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
 
 Diese Variable ist interessant, weil:  
 
@@ -68,5 +68,7 @@ plot(gfc$gdpeegdpuu)
 ![](demo_final_files/figure-html/unnamed-chunk-3-1.png)<!-- -->
 
 
+## Interpretation
 
+Aus der Analyse können wir ableiten, dass der Trend nach unten geht. Dies zeigt uns auch der Plot. Die ökonomischen Implikationen sind: