Quasi-Poisson and negative binomial regression models

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In this lecture, we will discuss quasi-Poisson and negative binomial regression models that can be used as an alternative to Poisson regression when the data show either under- or overdispersion.
1. Variance vs the mean and the Pearson residuals
2. Under and overdispersion (05:34)
3. Quasi-Poisson (06:39)
4. Negative Binomial regression (10:27)
5. Compare Poisson vs NB and QP (14:15)
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This is the best video about it I have ever seen! Thank you a lot.

viniciussilva

Your explanations are so clear. I found your videos extremely helpful. I wish you success as you deserve to have a few million views soon. :)

carolinacampos

Great video, crystal clear explanations!!

TheTrashcan

Great explanation. Thanks for being "ambassador of statistics" on youtube!

Мечтатель-хх

Wow, finally i understand the mechanisms behind these regression models. Thank you!

XtremeTerror

Thanks for the excellent video that illustrates how quasi-Poisson and NB regressions can address over-dispersion.

Just a question, is it appropriate to use NB regression when the overdispersion is large and the variance against the mean is about linear?

kby--b

Thank you! you solved many questions that I had.

PJZWGuNrRock

Thanks for your video!
I have to fit a negative binomial regression and want to calculate R^2. Which R^2 should be used?🙏 thanks in advance!

carinaeisenwagner

Destê te sax be ... Thank you very much...

re_mamoste

Hi, can I ask you about the interpretation of the coefficient at the end. The video says it highlights the usage of quasi-poisson and NB model instead of poisson model. Is it because poisson model doesn’t capture the over-dispersed data and includes the intercept as it shows the p-value less than 0.05?

po

Sir i want to ask to you. Is Quasi Poisson regression and Quasi Likelihood regression is different?🙏

Kvinnmanoppo

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