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Auto-generated via `{sandpaper}` Source : 84067ae Branch : main Author : Christian B. Knudsen <[email protected]> Time : 2024-11-12 12:51:23 +0000 Message : licens
chrbknudsen · Nov 12, 2024 · c98c1dd · c98c1dd
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diff --git a/clt.md b/clt.md
@@ -45,7 +45,7 @@ mean(random_numbers)
 ```
 
 ``` output
-[1] 0.4799895
+[1] 0.5487439
 ```
 The important point of the Central Limit Theorem is, that if we take a large
 number of random samples, and calculate the mean of each of these samples,
@@ -59,7 +59,7 @@ mean(runif(100))
 ```
 
 ``` output
-[1] 0.4575514
+[1] 0.4917325
 ```
 And we can use the `replicate()` function to repeat that calculation several times, in this case 1000 times:
 

diff --git a/fig/clt-rendered-random-histogram-1.png b/fig/clt-rendered-random-histogram-1.png
diff --git a/fig/clt-rendered-repeated-means-histogram-1.png b/fig/clt-rendered-repeated-means-histogram-1.png
diff --git a/kmeans.md b/kmeans.md
@@ -144,32 +144,32 @@ clustering
 ```
 
 ``` output
-K-means clustering with 3 clusters of sizes 62, 47, 69
+K-means clustering with 3 clusters of sizes 50, 28, 100
 
 Cluster means:
-   Alcohol Malicacid      Ash Alcalinityofash Magnesium Totalphenols Flavanoids
-1 12.92984  2.504032 2.408065        19.89032 103.59677     2.111129   1.584032
-2 13.80447  1.883404 2.426170        17.02340 105.51064     2.867234   3.014255
-3 12.51667  2.494203 2.288551        20.82319  92.34783     2.070725   1.758406
-  Nonflavanoidphenols Proanthocyanins Colorintensity       Hue
-1           0.3883871        1.503387       5.650323 0.8839677
-2           0.2853191        1.910426       5.702553 1.0782979
-3           0.3901449        1.451884       4.086957 0.9411594
-  OD280OD315ofdilutedwines   Proline
-1                 2.365484  728.3387
-2                 3.114043 1195.1489
-3                 2.490725  458.2319
+   Alcohol Malicacid    Ash Alcalinityofash Magnesium Totalphenols Flavanoids
+1 13.33680  2.396800 2.3718        18.51000  108.6000     2.432400   2.214800
+2 13.82214  1.773929 2.4900        16.96429  105.3571     2.923929   3.111429
+3 12.60250  2.463600 2.3293        20.69600   93.7400     2.050400   1.633500
+  Nonflavanoidphenols Proanthocyanins Colorintensity      Hue
+1           0.3236000        1.707200       5.143600 0.966720
+2           0.2985714        1.986786       6.202857 1.103571
+3           0.3987000        1.421900       4.694800 0.911900
+  OD280OD315ofdilutedwines Proline
+1                 2.862800  894.60
+2                 2.984643 1301.50
+3                 2.381700  517.75
 
 Clustering vector:
-  [1] 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 1 1 2 2 1 2 2 2 2 2 2 1 1
- [38] 2 2 1 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 1 3 1 3 3 1 3 3 1 1 1 3 3 2
- [75] 1 3 3 3 1 3 3 1 1 3 3 3 3 3 1 1 3 3 3 3 3 1 1 3 1 3 1 3 3 3 1 3 3 3 3 1 3
-[112] 3 1 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 1 3 3 1 1 1 1 3 3 3 1 1 3 3 1 1 3 1
-[149] 1 3 3 3 3 1 1 1 3 1 1 1 3 1 3 1 1 3 1 1 1 1 3 3 1 1 1 1 1 3
+  [1] 1 1 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 1 1 2 2 1 2 1 1 1
+ [38] 2 1 1 1 1 1 3 1 1 1 1 1 2 2 2 2 2 1 2 1 2 2 3 3 3 3 3 3 3 3 3 1 1 1 3 3 1
+ [75] 1 3 3 3 1 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3
+[112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 3 3 1 1 3 3
+[149] 3 3 3 3 3 3 3 1 3 1 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 1 1 1 3
 
 Within cluster sum of squares by cluster:
-[1]  566572.5 1360950.5  443166.7
- (between_SS / total_SS =  86.5 %)
+[1]  815783.7  550201.0 1263330.5
+ (between_SS / total_SS =  85.1 %)
 
 Available components:
 
@@ -188,9 +188,9 @@ table()
 ``` output
      true
 quess  1  2  3
-    1 13 20 29
-    2 46  1  0
-    3  0 50 19
+    1 30  9 11
+    2 28  0  0
+    3  1 62 37
 ```
 
 The algorithm have no idea about the numbering, the three groups are numbered 

diff --git a/md5sum.txt b/md5sum.txt
@@ -1,7 +1,7 @@
 "file" "checksum" "built" "date"
 "CODE_OF_CONDUCT.md" "c93c83c630db2fe2462240bf72552548" "site/built/CODE_OF_CONDUCT.md" "2024-11-12"
 "LICENSE.md" "b24ebbb41b14ca25cf6b8216dda83e5f" "site/built/LICENSE.md" "2024-11-12"
-"config.yaml" "ab3386f0cbf91528ac689e54989289c2" "site/built/config.yaml" "2024-11-12"
+"config.yaml" "5514a53c965971f5454e4152c8760485" "site/built/config.yaml" "2024-11-12"
 "index.md" "61394b6d8242b2ec5c69d9c2493cd74b" "site/built/index.md" "2024-11-12"
 "links.md" "8184cf4149eafbf03ce8da8ff0778c14" "site/built/links.md" "2024-11-12"
 "episodes/reproducible-analysis.Rmd" "976433176d38ef5f97b18f2b25794941" "site/built/reproducible-analysis.md" "2024-11-12"

diff --git a/normal-distribution.md b/normal-distribution.md
@@ -196,7 +196,7 @@ rnorm(5, mean = 0, sd = 1 )
 ```
 
 ``` output
-[1] -0.1404694 -0.2617336  0.2354110 -1.6658941 -2.2493942
+[1] -0.5854523  0.4889116  1.8896220  1.3496765  0.6922389
 ```
 Den returnerer (her) fem tilfældige værdier fra en normalfordeling med (her) 
 middelværdi 0 og standardafvigelse 1.