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I found this page useful, but would it be worth adding something about estimating dispersion parameters and their use in defining quasi-likelihood models? A little more on the causes of overdispersion would also be helpful. It's beyond me to add these things, but I'd certainly find it useful if someone else did! McB50 ( talk) 10:00, 19 April 2013 (UTC)
A statistician should rewrite this. The page has stuff backwards. 88.111.239.43 ( talk) 13:38, 2 August 2018 (UTC)
A statistician should rewrite this by taking the backwards view. Statisticians want to talk about the stuff they know how to do; what we want is to know is what is the best practice based on our use-cases. The problem is that there is only minimal over lap between the two. If I need to make a decision, I can bullshit it or I can find out what the stats say. IMHO the second is the right path. What I need to know is if the stats people have an opinion but when they don't, that is great; I know that I have no chance of finding something definitive and can relax and just use "best practice," whatever that is. I thus found this page extremely useful and as McB50 said, more discussion of overdispersion would be great. So lets Talk. It seems the variance for a coin toss _ought_to_ follow 1/(2^n) where n is the size of the sample. Thus, for a single coin toss, the probablility of all tosses resulting in a head is 0.5. For n=2, an all heads outcome has probability 0.25 and so on. If I confound the coin toss outcome with a second variable -- lets say a second coin --- the "over"dispersion (the variance) is going to be different.. suggesting that ... This article seems to references work that takes this approach? Does that mean we should view a Gaussian distribution is some kind of "pure" version of a binomial distribution?!!! ... we know that a coin toss should follow 1/(2^n) and when it doesn't we need to start looking for confounding factors like a second coin? Now that would be addressing the question I have about how to spot when a sample is not unimodal. — Preceding unsigned comment added by 31.125.39.26 ( talk) 18:45, 21 January 2021 (UTC)
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| This article is rated Start-class on Wikipedia's
content assessment scale. It is of interest to the following WikiProjects: | ||||||||||
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I found this page useful, but would it be worth adding something about estimating dispersion parameters and their use in defining quasi-likelihood models? A little more on the causes of overdispersion would also be helpful. It's beyond me to add these things, but I'd certainly find it useful if someone else did! McB50 ( talk) 10:00, 19 April 2013 (UTC)
A statistician should rewrite this. The page has stuff backwards. 88.111.239.43 ( talk) 13:38, 2 August 2018 (UTC)
A statistician should rewrite this by taking the backwards view. Statisticians want to talk about the stuff they know how to do; what we want is to know is what is the best practice based on our use-cases. The problem is that there is only minimal over lap between the two. If I need to make a decision, I can bullshit it or I can find out what the stats say. IMHO the second is the right path. What I need to know is if the stats people have an opinion but when they don't, that is great; I know that I have no chance of finding something definitive and can relax and just use "best practice," whatever that is. I thus found this page extremely useful and as McB50 said, more discussion of overdispersion would be great. So lets Talk. It seems the variance for a coin toss _ought_to_ follow 1/(2^n) where n is the size of the sample. Thus, for a single coin toss, the probablility of all tosses resulting in a head is 0.5. For n=2, an all heads outcome has probability 0.25 and so on. If I confound the coin toss outcome with a second variable -- lets say a second coin --- the "over"dispersion (the variance) is going to be different.. suggesting that ... This article seems to references work that takes this approach? Does that mean we should view a Gaussian distribution is some kind of "pure" version of a binomial distribution?!!! ... we know that a coin toss should follow 1/(2^n) and when it doesn't we need to start looking for confounding factors like a second coin? Now that would be addressing the question I have about how to spot when a sample is not unimodal. — Preceding unsigned comment added by 31.125.39.26 ( talk) 18:45, 21 January 2021 (UTC)