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Source  : bb9acbf
Branch  : main
Author  : Christian Knudsen <[email protected]>
Time    : 2024-12-10 08:30:20 +0000
Message : Merge pull request #194 from chrbknudsen/main

anova
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actions-user committed Jan 7, 2025
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4 changes: 2 additions & 2 deletions clt.md
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Expand Up @@ -45,7 +45,7 @@ mean(random_numbers)
```

``` output
[1] 0.5058251
[1] 0.5249431
```
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,
Expand All @@ -59,7 +59,7 @@ mean(runif(100))
```

``` output
[1] 0.4781602
[1] 0.5177501
```
And we can use the `replicate()` function to repeat that calculation several times, in this case 1000 times:

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46 changes: 23 additions & 23 deletions kmeans.md
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Expand Up @@ -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 28, 100, 50
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 13.82214 1.773929 2.4900 16.96429 105.3571 2.923929 3.111429
2 12.60250 2.463600 2.3293 20.69600 93.7400 2.050400 1.633500
3 13.33680 2.396800 2.3718 18.51000 108.6000 2.432400 2.214800
Nonflavanoidphenols Proanthocyanins Colorintensity Hue
1 0.2985714 1.986786 6.202857 1.103571
2 0.3987000 1.421900 4.694800 0.911900
3 0.3236000 1.707200 5.143600 0.966720
OD280OD315ofdilutedwines Proline
1 2.984643 1301.50
2 2.381700 517.75
3 2.862800 894.60
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] 3 3 1 1 3 1 1 1 3 3 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 1 1 3 3 1 1 3 1 3 3 3
[38] 1 3 3 3 3 3 2 3 3 3 3 3 1 1 1 1 1 3 1 3 1 1 2 2 2 2 2 2 2 2 2 3 3 3 2 2 3
[75] 3 2 2 2 3 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 3 2 2 3 3 2 2
[149] 2 2 2 2 2 2 2 3 2 3 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 3 3 3 3 2
Within cluster sum of squares by cluster:
[1] 566572.5 1360950.5 443166.7
(between_SS / total_SS = 86.5 %)
[1] 550201.0 1263330.5 815783.7
(between_SS / total_SS = 85.1 %)
Available components:
Expand All @@ -188,9 +188,9 @@ table()
``` output
true
quess 1 2 3
1 13 20 29
2 46 1 0
3 0 50 19
1 28 0 0
2 1 62 37
3 30 9 11
```

The algorithm have no idea about the numbering, the three groups are numbered
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80 changes: 40 additions & 40 deletions md5sum.txt
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@@ -1,41 +1,41 @@
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2 changes: 1 addition & 1 deletion normal-distribution.md
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Expand Up @@ -196,7 +196,7 @@ rnorm(5, mean = 0, sd = 1 )
```

``` output
[1] -1.1729250 -0.3603588 0.3229349 -1.1552451 0.4363199
[1] 1.1164374 -1.1967227 0.4946689 -1.0101639 -0.6332367
```
Den returnerer (her) fem tilfældige værdier fra en normalfordeling med (her)
middelværdi 0 og standardafvigelse 1.
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