Thank you for posting this. My findings are as follows:
Only 2 existing locations have > 100 performance. Both of these have: - No strange smell - Mint air - Adequate Feng Shui
Most other high performers (But sub 100) have the same properties. Addittionally the weird sounds of the high performers are either: - Eerie Silence - Otherworldly Skittering
This suggests it would be sensible to restrict ourselve to locations with these properties. This alone increases the average performance from 23.12 to 46.92
High values of murphys constant are bad, though the affect seems to become small at around 3.5. There is some evidence to suggest that amongst the high performing section (But not the others) too low a value of murphys constant would be counterproductive, though it is a small affect. It may be a statistical fluctuation. Restricting to locations with a value < 3.5 would leave an average of 62.77
A value of pi below the normal value also looks harmful, though one that is too high also looks counterproductive. It looks like a relatively modest effect though and I don’t wan to exclude too many possible locations, so I won’t exclude any locations based on the value of pi.
There are few high performing ones between latitude −38 and +38. There are few high performing ones around shortitude 0, and +-90 There are few high performing ones around deltitude 0, and +-90 But this might be caused by the small number of bases in these areas.
I then fitted a simple linear trigonometric model to the records that had the other properties that were identified with high performance. This gave the following model:
90.40498684665667+1.0177516945528096*sin(deltitude)+11.356095534597717*cos(deltitude)-0.17160136718146096*sin(shortitude)+14.734915658705445*cos(shortitude)-2.41427525418688*sin(latitude)-62.034325081735766*cos(latitude)+5.158741059290979*sin(longitude)+8.287780468342245*cos(longitude) Standard deviation of error is 13.19479724158273 The no of records was 180
I found that the standard deviation error was reduced when degrees were converted to radians using the local value of pi, so that is what was used.
This predicts that the following 12 possible locations have the best performance:
Looking at this further, by far the strongest effect is the latitude, and that looks more like a rectangular effect than a trigonometric one. Replacing the trigonometric fit with one that modelled a rectangulat latitude effect and no other yielded a model that explained most of the variation. By itself this looks better than the previous model.
The next biggest effect looks like it is due to variation in murphys constant. This looked vaguely quadratic.
The next biggest effect looked like it was due to variations in the value of Pi. It looked vaguely triangular, with the point ust below 3.15.
The next biggest effect looked like a vaguely sinusoidal variation due to the longitude.
Including all of these in a model yielded one with a standard deviation of 4.9, and predicted that the following 12 locations were the best:
This is currently my best estimate. As the predicted values are all < 100 I will have to file a report on this with the empires colonisation department in case there is every any interest in making another attempt, but I won’t risk the empresses’ rath by attempting to colonise any of them.
Thank you for posting this. My findings are as follows:
Only 2 existing locations have > 100 performance. Both of these have:
- No strange smell
- Mint air
- Adequate Feng Shui
Most other high performers (But sub 100) have the same properties. Addittionally the weird sounds of the high performers are either:
- Eerie Silence
- Otherworldly Skittering
This suggests it would be sensible to restrict ourselve to locations with these properties. This alone increases the average performance from 23.12 to 46.92
High values of murphys constant are bad, though the affect seems to become small at around 3.5. There is some evidence to suggest that amongst the high performing section (But not the others) too low a value of murphys constant would be counterproductive, though it is a small affect. It may be a statistical fluctuation. Restricting to locations with a value < 3.5 would leave an average of 62.77
A value of pi below the normal value also looks harmful, though one that is too high also looks counterproductive. It looks like a relatively modest effect though and I don’t wan to exclude too many possible locations, so I won’t exclude any locations based on the value of pi.
There are few high performing ones between latitude −38 and +38.
There are few high performing ones around shortitude 0, and +-90
There are few high performing ones around deltitude 0, and +-90
But this might be caused by the small number of bases in these areas.
I then fitted a simple linear trigonometric model to the records that had the other properties that were identified with high performance. This gave the following model:
90.40498684665667+1.0177516945528096*sin(deltitude)+11.356095534597717*cos(deltitude)-0.17160136718146096*sin(shortitude)+14.734915658705445*cos(shortitude)-2.41427525418688*sin(latitude)-62.034325081735766*cos(latitude)+5.158741059290979*sin(longitude)+8.287780468342245*cos(longitude)
Standard deviation of error is 13.19479724158273 The no of records was 180
I found that the standard deviation error was reduced when degrees were converted to radians using the local value of pi, so that is what was used.
This predicts that the following 12 possible locations have the best performance:
38730 VALUE:110.47214318466737
103420 VALUE:110.67976641439135
91328 VALUE:111.69109272372066
26403 VALUE:112.35848311090837
7724 VALUE:112.40205758474453
21409 VALUE:113.21091851443907
89352 VALUE:113.88444725998731
3090 VALUE:113.89351821821175
65317 VALUE:121.11038526877846
57364 VALUE:123.12690147352956
26627 VALUE:132.5469987591373
91627 VALUE:134.17450784571542
In practice I think it is unlikely that all of them will be greater than 100, but it looks like it will probably be good enough to please the empress.
Looking at this further, by far the strongest effect is the latitude, and that looks more like a rectangular effect than a trigonometric one. Replacing the trigonometric fit with one that modelled a rectangulat latitude effect and no other yielded a model that explained most of the variation. By itself this looks better than the previous model.
The next biggest effect looks like it is due to variation in murphys constant. This looked vaguely quadratic.
The next biggest effect looked like it was due to variations in the value of Pi. It looked vaguely triangular, with the point ust below 3.15.
The next biggest effect looked like a vaguely sinusoidal variation due to the longitude.
Including all of these in a model yielded one with a standard deviation of 4.9, and predicted that the following 12 locations were the best:
76804 VALUE:87.95301643603202
16965 VALUE:88.18566645580597
104815 VALUE:88.34280034001172
8415 VALUE:88.39346893009704
18123 VALUE:88.50303192064138
107929 VALUE:88.5221749787355
99595 VALUE:88.59004262250107
80395 VALUE:88.59313676878352
42742 VALUE:88.72736581213306
40639 VALUE:88.80584599223495
65607 VALUE:90.36919375244607
94304 VALUE:90.63981001558145
This is currently my best estimate. As the predicted values are all < 100 I will have to file a report on this with the empires colonisation department in case there is every any interest in making another attempt, but I won’t risk the empresses’ rath by attempting to colonise any of them.