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#1




which link functions are "easily interpretable"?
i know the log link relates the GLM coefficients to the % changes to the target variable, but which ones do NOT? How would i know whether they do or do not?

#2




The only easy ones are
1. identity  the coefficient is the average change in predicted y for 1 unit increase in x 2. logit  exp(coefficient) is the average change in odds of y for 1 unit increase in x 3. log  exp(coefficient) is the average multiplicative change in predicted y for 1 unit increase in x 
#3




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Are the following true? 1. The overall distribution (Gaussian, Gamma, Binomial, Poisson, QuasiPoisson) should be selected based on the distribution of the target variable, taking into consideration discretecontinues, positivenegative, skew. 2. The link function should be selected similar to the above. For example, a Gaussian is sufficient to model continuous variables without a skew, but should be modified to have a log link function if only positive results are desired. 3. Interpreting the coefficients is a function of the link function, but not the overall distribution. For example, coefficients for either a Gaussian, Gamma or Poisson distribution with a log link are best interpreted as the product of the predictor and the exponentiated coefficient Thank you very much for any lastminute help 
#4




Let's look just at #2 below:
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This is clearly a discrete variable, but we will certainly be happy with a continuous approximation. It can't be negative, so your reasoning above suggests that we should account for skewness. But really, that doesn't make any sense does it? True, you might want a skewness component to account for correlation, but not only because it is a nonnegative variable. If you know any statistical mechanics, you'll know of many other examples.
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#5




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So I suppose the lesson you're imparting regarding #2 is that while those rules are technically in place, they need to be met with consideration and can be bent. Quote:
*Edited post to swap out the word "model" for "plot" Last edited by Relmiw; 12102019 at 09:33 PM.. 
#6




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For example, that ER was not log transformed due to its few distinct values in hospital readmission project. Also it's due to the majority of ER being 0 that could not be log transformed. For votes, if it's at aggregate level such as city and state count, then we may think about GLM Poisson with offset. If it's at some average per case level, then we may consider GLM Poisson with weight instead. Offset has been tested in last December's exam, but weight has not been tested before except in a short online module example. 
#7




Is there going to be a case where we're asked to interpret inverse/cloglog or any of those link functions? is it worth worrying about them?
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#8




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