Understanding Generalized Linear Models (Logistic, Poisson, etc.)

Learning Objectives:
#1.Understand when to use GLMS
#2. Know the three components of a GLM
#3. Difference between transformation and a link function
#4. Know when to use logistic, poisson, gamma, etc.

[NicholasRenotte]

That introduction though 😂 I have never seen someone so excited to be asked about GLMs.

[PortugueseAfrican]

I've encountered GLMs for years, this was the best explanation I've ever seen. Well done and thank you for your service! 👏🙇‍♂️

[jakobudovic]

i wish every professor was like you. how you kept my attention was amazing.

[TheNeocalif]

You are a fabulous professor, ur students are lucky

[jackskellington]

I'm an actuary and we work with GLMs every day! Great explanation.

[jeanpompeo]

Honestly, thank you so much for this explanation!! It's super super helpful to have someone actually explain the different types of glm's in a easy to understand way. I had not idea what they were nor when to use them, and now I don't have to keep bashing my head against a wall trying to understand the world of statistics :)

[pythoninoffice]

I spent hours and hours trying to understand GLM from text books and still came out confused. Your 20 mins video cleared everything up. THANK YOU!

[IsaacJolayemi]

Your value is more than your appearance
You are amazing.
Thanks for rapping me to the point of the truth regarding GLM

[icemanrocks]

This is the best video I have ever watched on the Internet. Thank you so much for sharing your insights with the research community. God bless you, sir!!!

[yolojourney]

You are so good at keeping up attention, which i think is so important for people teaching! Keep up the good work!

[galenseilis]

The negative binomial distribution is obtained by the compound distribution of a Poisson distribution with Gamma-distributed inter-arrival times. It generalizes the Poisson distribution to have over-dispersion (i.e. the mean being less than the variance). The negative binomial cannot give underdispersion where the variance is less than the mean, but this can be achieved using the generalized Poisson distribution.

[galenseilis]

I use a generalization of Poisson regression called inhomogenous Poisson point process regression. It is useful for modelling arrivals of discrete units into a system over time.

[zehuiliu]

You are awesome. It takes only a few minutes to let me understand why GLM is so important. Love your lecture.

[dataman]

Seriously good, you are demystifying many issues I have struggled to understand

[chiawenkuo]

Thank you for the brief but clear explanation about different "distributions".

[tomaswust]

Extremely helpful video ! Thank you for your clear explanations

[ericpenarium]

why am I just NOW finding you. love the style! 2:20 is my style.

[angelajcabul]

Thanks for your explanation! If you have some examples how to apply them, it would be extremly helpful! Thanks a lot.

[edwinjesuspaleta]

Man this video was great. I do get the excitement for GLMs tho, i actually got significant results using that and not a student T as suggested by my tutor.

[ProjectNomad]

You are great! And I love music in the background, gives a crazy feeling which eases up information for some reason.