So this boils down to interpreting scatter charts.
Say you plot two normally-distributed numbers against one another. You get something that looks like this:
If instead you plot two d6 rolls against one another, you see this:
with sharp cutoffs because the d6 roll is bounded at 1 below and 6 above, and with a regular grid because the d6 roll is always an integer.
Various relationships between the variables can show up in the scatter chart
If Y is the sum of two d6 rolls, and X is the first roll, you see this:
You can think of this graph as being made up of various stripes:
The vertical green line is ‘every value the second die can roll, given that the first die rolled a 2’.
The diagonal orange line is ‘every value the first die can roll, given that the second die rolled a 4’.
Suppose that X = twice the first die plus the second die, and Y = twice the second die plus the first die:
Again the points form a grid, and again we can see patterns. Since the green line has 6 points on it and moves [up 2 and right 1] each step, we can see something that takes 6 discrete values and applies 2x its value to Y and 1x its value to X.
Now plot Bella’s scores against Liboulen’s:
This is a bit more complicated because there are three variables rather than two. But you can still imagine the same lines:
and you can disentangle the corresponding variables.
making a scatter-plot of Colleen vs Liboulen’s predictions. You can see that this plot has the points on a “flattened prism” in 3 directions, and manually count the shifts and see that each of the underlying components has 10 possible values.
Once you have that structure, you can pick out points on the extremes and use their slopes to calculate some of the relevant slopes. Finally, I brought in Bella’s info and used that to work out the remaining stats. (I used chatGPT for some help throwing together some linear regressions, but they needed a good bit of tweaking to be functional, and mostly agreed with the slopes that I had calculated by just looking at the scatterplots.)
Could you please explain how you inferred the existence of A B and C? I’d like to know more.
So this boils down to interpreting scatter charts.
Say you plot two normally-distributed numbers against one another. You get something that looks like this:
If instead you plot two d6 rolls against one another, you see this:
with sharp cutoffs because the d6 roll is bounded at 1 below and 6 above, and with a regular grid because the d6 roll is always an integer.
Various relationships between the variables can show up in the scatter chart
If Y is the sum of two d6 rolls, and X is the first roll, you see this:
You can think of this graph as being made up of various stripes:
The vertical green line is ‘every value the second die can roll, given that the first die rolled a 2’.
The diagonal orange line is ‘every value the first die can roll, given that the second die rolled a 4’.
Suppose that X = twice the first die plus the second die, and Y = twice the second die plus the first die:
Again the points form a grid, and again we can see patterns. Since the green line has 6 points on it and moves [up 2 and right 1] each step, we can see something that takes 6 discrete values and applies 2x its value to Y and 1x its value to X.
Now plot Bella’s scores against Liboulen’s:
This is a bit more complicated because there are three variables rather than two. But you can still imagine the same lines:
and you can disentangle the corresponding variables.
Thank you very much! This is very clear!
I was able to deduce them by
making a scatter-plot of Colleen vs Liboulen’s predictions. You can see that this plot has the points on a “flattened prism” in 3 directions, and manually count the shifts and see that each of the underlying components has 10 possible values.
Once you have that structure, you can pick out points on the extremes and use their slopes to calculate some of the relevant slopes. Finally, I brought in Bella’s info and used that to work out the remaining stats. (I used chatGPT for some help throwing together some linear regressions, but they needed a good bit of tweaking to be functional, and mostly agreed with the slopes that I had calculated by just looking at the scatterplots.)