Thanks for the correlation plots. These are quite useful and address the argument that was proposed.
It is worth looking at this topic more closely.
The first and foremost thing about correlation is that correlation does not point to causality.
However, we should try to understand where the general positive correlation is coming from.
In this exercise we are comparing positive numbers only.
Furthermore, there is a clear influence from the varying page size:
- short pages typically have few ch words and sh words
- long pages can have more ch words and sh words
This automatically causes a trend with the model line going up for increasing numbers, and crossing the origin very close to (0,0).
This can be seen in essentially all plots. The dotted line behaves like this. It's slope is always close to the ratio:
nr of sh-words divided by nr of ch-words.
Let's look at the plot for all words per page. The correlation coefficient is given as +0.55
This might seem a strong positive correlation but this is quite misleading.
In the low-area word count there is a large group where there is basically no correlation. All combinations of counts occur.
Interestingly, there is also a considerable group of pages where the sh-words are more frequent than the ch-words. These tend to be the longer pages. These two things together are already causing something like a positive correlation.
To illustrate the fact that varying page length is dominating these results, lets look at the hypothetical case that the Voynich manuscript text were composed by picking words arbitrarily from a hat. In this case:
- more total words on a page -> more ch words on a page
- more total words on a page -> more sh words on a page
This causes a positive correlation between ch-words and sh-words, without any causality.
(Clearly in this case the text would be meaningless, but not the result of auto-copying).
Because of all this, it is also interesting to look at the pair chol / chor.
The reason is that these words are relatively infrequent on the longer pages, which are all in Currier-B. As a result, the variation of absolute counts per page is much more limited.
The correlation for this case is given as +0.44
This seems like a high positive correlation but for people used to working with correlation coefficients, it is not.
The figure is included here for visual verification that for any number of chol counts, the number of shol counts can vary considerably.
It is worth looking at this topic more closely.
The first and foremost thing about correlation is that correlation does not point to causality.
However, we should try to understand where the general positive correlation is coming from.
In this exercise we are comparing positive numbers only.
Furthermore, there is a clear influence from the varying page size:
- short pages typically have few ch words and sh words
- long pages can have more ch words and sh words
This automatically causes a trend with the model line going up for increasing numbers, and crossing the origin very close to (0,0).
This can be seen in essentially all plots. The dotted line behaves like this. It's slope is always close to the ratio:
nr of sh-words divided by nr of ch-words.
Let's look at the plot for all words per page. The correlation coefficient is given as +0.55
This might seem a strong positive correlation but this is quite misleading.
In the low-area word count there is a large group where there is basically no correlation. All combinations of counts occur.
Interestingly, there is also a considerable group of pages where the sh-words are more frequent than the ch-words. These tend to be the longer pages. These two things together are already causing something like a positive correlation.
To illustrate the fact that varying page length is dominating these results, lets look at the hypothetical case that the Voynich manuscript text were composed by picking words arbitrarily from a hat. In this case:
- more total words on a page -> more ch words on a page
- more total words on a page -> more sh words on a page
This causes a positive correlation between ch-words and sh-words, without any causality.
(Clearly in this case the text would be meaningless, but not the result of auto-copying).
Because of all this, it is also interesting to look at the pair chol / chor.
The reason is that these words are relatively infrequent on the longer pages, which are all in Currier-B. As a result, the variation of absolute counts per page is much more limited.
The correlation for this case is given as +0.44
This seems like a high positive correlation but for people used to working with correlation coefficients, it is not.
The figure is included here for visual verification that for any number of chol counts, the number of shol counts can vary considerably.
![[+]](https://www.voynich.ninja/images/collapse_collapsed.png)