Poisson regression - clearly explained 

Thank you! Maybe my video on multiple linear regression might help you to interpret a model with 2 explanatory variables. ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-AP_K7SaKkIE.html

Thank you!

Great! Yes, there are 6 more videos about Poisson regression on my channel.

Great!

3rd request for gamma regression

Thank you! Yes, log can be confusing.

I think this page explains it in a simple way: www.statology.org/poisson-confidence-interval/

@@tilestats brilliant, thank you. But one thing is unclear to me. For lower bound, α/2 is replaced by .975. However, α/2 is not .975 if α=.05. Should it be 1-(α/2) for lower bound and α/2 for upper bound?

Yes, it seems to be incorrect. It should be: upper: 1-(α/2) = 0.975 Lower: (α/2) = 0.025 if α = 0.05

That is explained in this video ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-5GJR81WgLN0.html

Because exp dist models a continuous variable, which may take negative values. For example, if you measure the concentration of a drug, which decays exponentially, the concentration will approach zero. When the concentration is close to zero, the instrument that you measure with may result in negative values. However, you can use another distribution for the error term if that fits your data better.

@@tilestats in the example presented in your video if we assume an exponential distribution in the error terms then could we model this way instead of a poisson regression?

I put that on my list. I have a set of basic lectures to do first.

Because the data points around the fitted curve follow a Poisson distribution.

Do you refer to the Poisson distribution ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-FKZ1cgqh-_Q.html ?

​@@tilestats yes I think so. Can't remember for sure.

If you have a question related to the video, you can ask here.

To calculate the predicted counts you should, as you say, of course, use the equation but the whole idea was to explain how to interpret the coefficients in Poisson regression, not explain how to calculate the predicted counts.

​@@tilestats Thank you. One last ques: I'm a bit uncertain when you say the variance has to be equal to the mean. Does this mean that the mean of all the Y values of the points on the graph at 2:31 must be equal to the variance of those same Y values shown on the graph represented by the dots? in other words do you mean that the mean of all the Y values (all the actual observed counts not predicted counts) on a scatter plot must be equal to the variance of all those Y values? Or is it just the mean of the observed Y values for a certain week ( a certain x value) than must equal the variance of the Y values for that particular week. thanks

This video hopefully explains it ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-jnKDQtDy0Bg.html