- Joined
- Jan 24, 2003
- Messages
- 5,242
sometimes there are posts you just wish you could take back. This is one of them.
I am just gonna get myself deeper and deeper and deeper into a place I don't want to go
To answer your question I need to use even more jargon I'm afraid. Sorry.
The residuals are normalised, which basically means that a residual of 2 indicates that the data point in this example is around 5000 Jason points from the fitted curve. In the fitted data example, the data set is naturally split into two subsets and the two subsets do indeed represent the winning games and the losing games. The reason for this is that the losing games have a higher residual, they are further away from the fitted line (Y0).
The fitted line is weighted heavily for winning games as there were more of them. So a data point at X=7000 with a residual of -2 represents a Jason score of around 2000. The two subsets of data also have opposite gradients because for a winning game the fewer turns you take, generally the higher score you get, whilst in a losing game the longer you survive the higher score you get.
Because there is no win bonus for a losing game teh only source of variation is the rate at which an individual game accumulates Firaxis points, and this is why the losing game subset is very nearly linear.
I am up to my elbows in sh!$..... but my hands are above my head!
I really am not a statistician. Be nice and let me off the hook now. Please?
EDIT:
I just looked at the charts again, and I did actualy remember to scale the Y axis with the gross residuals (not normalised as stated above). So a data point at X = 7000 with a residual of -5000 represents a Jason score of 2000. Sorry for any
BTW you guessed right... I am an automotive engineer by trade. Why do so many engineers play CIV?


To answer your question I need to use even more jargon I'm afraid. Sorry.

The residuals are normalised, which basically means that a residual of 2 indicates that the data point in this example is around 5000 Jason points from the fitted curve. In the fitted data example, the data set is naturally split into two subsets and the two subsets do indeed represent the winning games and the losing games. The reason for this is that the losing games have a higher residual, they are further away from the fitted line (Y0).
The fitted line is weighted heavily for winning games as there were more of them. So a data point at X=7000 with a residual of -2 represents a Jason score of around 2000. The two subsets of data also have opposite gradients because for a winning game the fewer turns you take, generally the higher score you get, whilst in a losing game the longer you survive the higher score you get.
Because there is no win bonus for a losing game teh only source of variation is the rate at which an individual game accumulates Firaxis points, and this is why the losing game subset is very nearly linear.
I am up to my elbows in sh!$..... but my hands are above my head!

EDIT:

I just looked at the charts again, and I did actualy remember to scale the Y axis with the gross residuals (not normalised as stated above). So a data point at X = 7000 with a residual of -5000 represents a Jason score of 2000. Sorry for any

BTW you guessed right... I am an automotive engineer by trade. Why do so many engineers play CIV?