Man, incredible content! Thank you very much. I'm an mechanical engineer trying to do statistics and this is helping SO much. You deserve and will have more views in the future, keep it up.
Why not a poison or negative binomial distribution? Since you’ve got # minutes (aka time, continuous) and a discrete observation (dead, alive) then couldn’t a poisson or neg binomial distribution also make sense?
I think it is because the variable being modelled has a bernoulli distribution (dead, alive). In addition, both poisson and negative binomial only have integer values, so I don't think they are appropriate for modelling continuously measured time.
Man, incredible content! Thank you very much. I'm an mechanical engineer trying to do statistics and this is helping SO much. You deserve and will have more views in the future, keep it up.
It's actually marvelous0 that you used kinda "real" data and showcased that sometimes a certain model may fit the data extremely poorly.
Careful, when you start you can't stop binging his content!
Caution to be especially advised after coffee
Would be great to have access to the other GLIM regression examples. BTW very nice synthetic functions in Flexplot
I wish you were my stats professor 😭
how to calculate statistical significance if we have a hypothesis? Like willpower is associated with died/not died
Why not a poison or negative binomial distribution? Since you’ve got # minutes (aka time, continuous) and a discrete observation (dead, alive) then couldn’t a poisson or neg binomial distribution also make sense?
I think it is because the variable being modelled has a bernoulli distribution (dead, alive). In addition, both poisson and negative binomial only have integer values, so I don't think they are appropriate for modelling continuously measured time.
After a few people died in the battle, the snap made 50% of the survivors disappear.
Ha!
How would we incorporate a random variable in the full and reduced model, as well as the model comparison (line 26)? Something like this?
full_model