I appreciate all of these videos. You giving the interpretation of the numbers and spelling out the logic helps significantly! Thank you so much for your time and efforts here!
Clarification: the Poisson coefficient beta is the difference between the log expected counts beta=log(mu_(x+1))-log(mu_x). Reworking this expression the percent change in counts equals (mu_(x+1)-mu_x)/mu_x=exp(beta)-1. That's why at 3:00 and 7:09 in the video, I interpreted the coefficient approximately as a percent change in counts.
Miss Katchova, your videos are marvelous. Your explanations are very clear. Why haven't you uploaded more videos recently? It would be great a mini - series of videos dedicated to random parameters and fixed parameters models. Greeting from Colombia.
I appreciate all of these videos. You giving the interpretation of the numbers and spelling out the logic helps significantly! Thank you so much for your time and efforts here!
Clarification: the Poisson coefficient beta is the difference between the log expected counts beta=log(mu_(x+1))-log(mu_x). Reworking this expression the percent change in counts equals (mu_(x+1)-mu_x)/mu_x=exp(beta)-1. That's why at 3:00 and 7:09 in the video, I interpreted the coefficient approximately as a percent change in counts.
Very lucid presentation. Keep up the good work
Miss Katchova, your videos are marvelous. Your explanations are very clear. Why haven't you uploaded more videos recently? It would be great a mini - series of videos dedicated to random parameters and fixed parameters models. Greeting from Colombia.
This is exactly what I was looking for! Thanks! Liked! :)
Thank you for the video
thank you so much for helping this is absolutely helpful!
very good.
very helpful!