hi,
maybe I misunderstand the problem but the answer seems to be P=1-P(0) where P(0) is exp(-lambda) lambda beeing the mean.
regards
Hi all,
I have some data that includes the output of a Poisson Log Link model, that aims to predict how many times an event will occur over the period of a year. I would like to approximate the probability of at least one event occurring from this output, though I am not sure how to go about it. I can get hold of the mean/base of the Poisson model very easily, which I'm guessing would be needed in any calculation.
Any help would be massively appreciated.
Thanks,
Skinicod.
hi,
maybe I misunderstand the problem but the answer seems to be P=1-P(0) where P(0) is exp(-lambda) lambda beeing the mean.
regards
I think I may not have explained very well, so let me put the question in context.
A Poisson Log Link model has been built to predict the number of items that a customer may buy if marketed to.
For each customer that has been marketed to, I have been provided with the models predicted frequency for the number of items that they will buy. However, I am more interested in answering the question - will they buy anything at all. If I were building a model to predict this, I would use a logistic regression, however all I have is the output of the frequency model.
I am therefore trying to find a way to approximate one from the other. Is this possible?
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