Count Data Models in R

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Poisson Model, Negative Binomial Model, Hurdle Models, Zero-Inflated Models in R
  1. econometricsacademy
  2. Truncated negative binomial
  3. Negative binomial regression
  4. Hurdle models
  5. Econometrics in R
  6. Poisson model
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Комментарии
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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!

bradleyanderson
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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.

jorgebolivar
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Very lucid presentation. Keep up the good work

solomondensu
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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 That's why at 3:00 and 7:09 in the video, I interpreted the coefficient approximately as a percent change in counts.

econometricsacademy
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This is exactly what I was looking for! Thanks! Liked! :)

TimSter
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thank you so much for helping this is absolutely helpful!

wafiahhanin