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This is a very interesting take on it, I’ll openly admit that why I am politically incorrect I say merry christmas god bless america if this offends you I dont care . This whole thing about US amazing AA. I am not sure it holds up anymore. At tier 10, the Hindenburg arguably has the best AA and at tier 9, the Dmitri will give them. I don’t think they necessarily are, a mixed model could refer to a design with both a between and within subjects manipulation. Mixed-effects models are a type of regression analysis, often used for repeated measures design. Do you have some output you could post?. I’m using the MIXED procedure within SPSS as per StatNoodle’s recommendation on that previous post, which is the regression analysis (“mixed effects model” as you call it). Having to exclude the missing cases is one of the things.
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At tier 8, the MK’s AA performs better than the NO and the Hipper and Chepayev are not that far behind either I am politically incorrect I say merry christmas god bless america if this offends you I dont care . It sort of goes on like that as far as I can tell. Basically, the USN CAs are no more a no-fly zone than some of the other cruisers. Only the IJN cruisers are really bad. At high tier, MOST ships can heal. Fire damage is the most repairable damage. This is also likely why the midway is so behind now. Unfortunately, some ships are just less user-friendly. I especially like that you don’t have to exclude missing cases. Glad to help! The mixed process is so much more flexible than the traditional repeated measures, and mathematically superior when configured correctly. However, that flexibility can also be its downfall. The problem with. US cruisers shine at 8-12 km in a bow on small engagement, or stern on asymmetrical strike. IJN cruisers are bullies. You light BB on fire while there is nothing they can do about it, and let the damage tick away. They aren’t terribly good at cruiser duels if the opponent knows how to use their ship, but you can kite as many. Sometimes the CIs do not overlap so it’s easy enough to conclude they are significantly different from that (example, vs pre palm in the above image) but other times they do overlap and it’s not as clear-cut (example, Aknee vs Apalm in the above image). I really do hope I can make this work in SPSS. As you can imagine, in medical research on humans there are always some people who show up for the preop exam but not for the postop exam or stuff like that.
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