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GodMark2 · 8th September 2008, 03:17 PM

Originally Posted by Robin

This means that this factor is always the highest at the end of a run of calm's and lowest at the end of a run of emotionals - as you can see in this graph:

http://lh6.ggpht.com/robin1658/SMSZ0...ation.png.jpg"

Originally Posted by Robin

This means that whenever there is a run of "calm" the first "emotional" is guaranteed to be a local maximum, similarly the first "calm" after a run of "emotional" data will be a local minimum.

If you plan to publish, I think you can be a bit more specific and say that every local maximum will be "emotional", and every local minimum will be "calm". If an event was "calm", then it's "anticipation" must be lower than the following (as a calm event will raise the "anticipation"), thus not being able to be a local maximum. Reverse for "emotional". That statement can be used to directly prove that the effect observed is a mathematical artifact. You only need to prove that events that are neither locally maximal nor minimal are distributed randomly (which should be evident from your algorithm).

8th September 2008, 03:17 PM	#10
GodMark2 Master Poster Join Date: Oct 2005 Location: Oregon, USA Posts: 2,261	Originally Posted by Robin This means that this factor is always the highest at the end of a run of calm's and lowest at the end of a run of emotionals - as you can see in this graph: http://lh6.ggpht.com/robin1658/SMSZ0...ation.png.jpg" Originally Posted by Robin This means that whenever there is a run of "calm" the first "emotional" is guaranteed to be a local maximum, similarly the first "calm" after a run of "emotional" data will be a local minimum. If you plan to publish, I think you can be a bit more specific and say that every local maximum will be "emotional", and every local minimum will be "calm". If an event was "calm", then it's "anticipation" must be lower than the following (as a calm event will raise the "anticipation"), thus not being able to be a local maximum. Reverse for "emotional". That statement can be used to directly prove that the effect observed is a mathematical artifact. You only need to prove that events that are neither locally maximal nor minimal are distributed randomly (which should be evident from your algorithm).
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