You seem to be using the r value like a blunt instrument, the very thing you warned people using BG as.
"While 'r' (correlation coefficient) is a powerful tool, it has to be handled with care.
- The most used correlation coefficients only measure linear relationship. It is therefore perfectly possible that while there is strong non linear relationship between the variables, r is close to 0 or even 0. In such a case, a scatter diagram can roughly indicate the existence or otherwise of a non linear relationship.
- One has to be careful in interpreting the value of 'r'. For example, one could compute 'r' between the size of shoe and intelligence of individuals, heights and income. Irrespective of the value of 'r', it makes no sense and is hence termed chance or non-sense correlation.
- 'r' should not be used to say anything about cause and effect relationship. Put differently, by examining the value of 'r', we could conclude that variables X and Y are related. However the same value of 'r' does not tell us if X influences Y or the other way round. Statistical correlation should not be the primary tool used to study causation, because of the problem with third variables."
https://explorable.com/statistical-correlation
As I look at the graphs, ALL of the Under 300 CVD deaths per 100,000 occur at about, or above, a mean cholesterol of 4.5. and carry on up to just over 6.0.
On the other hand VIRTUALLY ALL mean cholesterol values of Under 4.5 are Over 300 CVD deaths per 100,000.
All of which makes me happy with my total of 5.2 (trigs 1.2)
Geoff