differences for PR #176

clt.md

@@ -45,7 +45,7 @@ mean(random_numbers)
 ```
 
 ``` output
-[1] 0.5192774
+[1] 0.4994345
 ```
 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] 0.4992043
+[1] 0.5301663
 ```
 And we can use the `replicate()` function to repeat that calculation several times, in this case 1000 times:
 

data.md

@@ -1021,6 +1021,14 @@ _Dimensions:_ Rows: 631 Columns: 5
 
 ### SEXRAT
 
+Frequencies of different sex orders of the first 5 children born in families.
+
+Does the probability of a male birth differ from 50%?
+
+Are the sex distribution of successive offspring independent? Ie, does the 
+sex of the first born child, affect the probability of the second child?
+
+
 _Dimensions:_ Rows: 60 Columns: 8   
 
 [source](data.md#rosner_1)^1^
@@ -1043,8 +1051,6 @@ _Dimensions:_ Rows: 60 Columns: 8
 |  sexchldn*  |  Sex of all children  |
 |  num_fam**  |  Number of families   |
 
-::::
-
 + For families with 5+ children, the sex of the first 5 children are listed.
 The number of children is given as 5 for such families.
 
@@ -1054,6 +1060,78 @@ such families.
 
 ** Number of families with specific gender contribution of children
 
+Example; there are:
+
+* 4400 families with 2 children where both children are male, 
+* 4270 families with 2 children where the first child is male, and the second female and,
+* 4633 families with 2 children where the first child is female and the second male.
+
+::::
+
+
+:::: spoiler
+
+## Example
+
+Compare P(child 2 is male | child 1 is female) with P(child 2 is male | child 1 is male)
+
+That is, the probability child 2 is male given that child 1 is female.
+
+```r
+sexrat <- read_csv("https://raw.githubusercontent.com/KUBDatalab/R-toolbox/main/episodes/data/SEXRAT.csv")
+
+# Number of families with female first child:
+
+sexrat %>% 
+  filter(sx_1 == "F") %>% 
+  summarise(nF1 = sum(num_fam))
+
+# A tibble: 1 x 1
+nF1
+<dbl>
+  1 25719
+
+# Number of those families with a male second child:
+
+sexrat %>% 
+  filter(sx_1 == "F",
+         sx_2 == "M") %>% 
+  summarise(nF1M2 = sum(num_fam))
+
+# A tibble: 1 x 1
+nF1M2
+<dbl>
+  1 12882
+
+# Point estimate for probability of child 2 being male, given child 1 is female:
+
+pF1M2 <- 12882/25719
+pF1M2
+
+[1] 0.5008748
+
+# Standard error of mean for proportions:
+
+SEM_F1M2 <- sqrt(pF1M2*(1-pF1M2)/25719)
+SEM_F1M2
+[1] 0.003117757
+
+# That gives us a 95% confidence interval for P(Child 2 is male | Child 1 is female):
+
+pF1M2 + c(-1,1)*1.96*SEM_F1M2
+
+[1] 0.4947640 0.5069856
+# Doing the same calculations for P(Child 2 is male |  Child 1 is male)
+# gives us an interval of (rounded):
+
+[1] 0.512 0.524
+
+# Which would indicate the having a male child first, increases the probability
+# of having a second male child.
+```
+
+::::
+
 
 ### SMOKE
 
@@ -1507,6 +1585,10 @@ Den oprindelige kilde til det datasæt: https://www.who.int/teams/global-tubercu
 
 <a id="rosner_1">1</a>: Rosner, Bernard A. Fundamentals of Biostatistics, 7/e, International Edition, 2011 ISBN: 9780538735896. https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9780538733496&token
 
+der er også guf her https://www.doc88.com/p-5925003681540.html
+
+https://statanaly.com/wp-content/uploads/2023/04/Fundamentals-of-Biostatistics-7th-Edition.pdf
+
 <a id="hopper_2">2</a>: Hopper, J.H. & Seeman, E (1994). The bone density
 of female twins discordant for tobacco use. New England Journal of Medicine, 330, 387-392.
 

kmeans.md

@@ -144,31 +144,31 @@ clustering
 ```
 
 ``` output
-K-means clustering with 3 clusters of sizes 69, 47, 62
+K-means clustering with 3 clusters of sizes 62, 47, 69
 
 Cluster means:
    Alcohol Malicacid      Ash Alcalinityofash Magnesium Totalphenols Flavanoids
-1 12.51667  2.494203 2.288551        20.82319  92.34783     2.070725   1.758406
+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 12.92984  2.504032 2.408065        19.89032 103.59677     2.111129   1.584032
+3 12.51667  2.494203 2.288551        20.82319  92.34783     2.070725   1.758406
   Nonflavanoidphenols Proanthocyanins Colorintensity       Hue
-1           0.3901449        1.451884       4.086957 0.9411594
+1           0.3883871        1.503387       5.650323 0.8839677
 2           0.2853191        1.910426       5.702553 1.0782979
-3           0.3883871        1.503387       5.650323 0.8839677
+3           0.3901449        1.451884       4.086957 0.9411594
   OD280OD315ofdilutedwines   Proline
-1                 2.490725  458.2319
+1                 2.365484  728.3387
 2                 3.114043 1195.1489
-3                 2.365484  728.3387
+3                 2.490725  458.2319
 
 Clustering vector:
-  [1] 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 2 2 3 3 2 2 3 2 2 2 2 2 2 3 3
- [38] 2 2 3 3 2 2 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 3 1 3 1 1 3 1 1 3 3 3 1 1 2
- [75] 3 1 1 1 3 1 1 3 3 1 1 1 1 1 3 3 1 1 1 1 1 3 3 1 3 1 3 1 1 1 3 1 1 1 1 3 1
-[112] 1 3 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 3 1 1 3 3 3 3 1 1 1 3 3 1 1 3 3 1 3
-[149] 3 1 1 1 1 3 3 3 1 3 3 3 1 3 1 3 3 1 3 3 3 3 1 1 3 3 3 3 3 1
+  [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
 
 Within cluster sum of squares by cluster:
-[1]  443166.7 1360950.5  566572.5
+[1]  566572.5 1360950.5  443166.7
  (between_SS / total_SS =  86.5 %)
 
 Available components:
@@ -188,9 +188,9 @@ table()
 ``` output
      true
 quess  1  2  3
-    1  0 50 19
+    1 13 20 29
     2 46  1  0
-    3 13 20 29
+    3  0 50 19
 ```
 
 The algorithm have no idea about the numbering, the three groups are numbered 

md5sum.txt

@@ -30,7 +30,7 @@
 "instructors/instructor-notes.md" "5cf113fd22defb29d17b64597f3c9bc0" "site/built/instructor-notes.md" "2024-11-12"
 "learners/CLT-dk.md" "a1852fcb44235823d23cd4a4af6d3d49" "site/built/CLT-dk.md" "2024-11-12"
 "learners/CLT-en.md" "8ae8f14f05472820ef155acf980ae06f" "site/built/CLT-en.md" "2024-11-12"
-"learners/data.md" "862bdded52e397453ca7ca4c9ecb6d0e" "site/built/data.md" "2024-11-12"
+"learners/data.md" "c0c18402a14da78b51c444e502637ea8" "site/built/data.md" "2024-11-12"
 "learners/reference.md" "527a12e217602daae51c5fd9ef8958df" "site/built/reference.md" "2024-11-12"
 "learners/setup.md" "9b1b924cf88e06b154562a92250fcb76" "site/built/setup.md" "2024-11-12"
 "poster/poster_dk.Rmd" "721a2b68eeb0b61308158bce41c6ae21" "site/built/poster_dk.md" "2024-11-12"

normal-distribution.md

@@ -196,7 +196,7 @@ rnorm(5, mean = 0, sd = 1 )
 ```
 
 ``` output
-[1] -1.8124108 -0.9414055 -0.1999997  1.6756810  0.8498545
+[1] -1.18196702 -0.21613964  0.23521930 -0.81630292  0.04482079
 ```
 Den returnerer (her) fem tilfældige værdier fra en normalfordeling med (her) 
 middelværdi 0 og standardafvigelse 1.