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

[@NicholasRenotte]

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

[@GraceRosburg-Francot]

Hello! I'm a graduate student in ecology with a terrible statistics background. Your videos are incredibly intuitive and make statistics less of a black box. Thanks so much for sharing your knowledge!

[@QuantPsych]

Thank you!!!

[@PortugueseAfrican]

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

[@TheNeocalif]

You are a fabulous professor, ur students are lucky

[@pythoninoffice6568]

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!

[@comatose_e]

but this video isn't teaching the deepth of GLM, it didn't explain the methods applied for the regression adjust over the link function, the IRLS algorithm for example

[@jackskellington4443]

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

[@OakQueso]

I’m an actuary too haha. Except I’m not working with GLMs because I’m at a start up commercial lines carrier and we aren’t too sophisticated yet. It would be nice to actually see the material I learned on p, MAS I, and II in practice!

[@yolojourney2961]

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

[@dataman1000]

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

[@jakobudovic]

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

[@QuantPsych]

Thanks! 😃

[@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!!!

[@zehuiliu8150]

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

[@chiawenkuo]

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

[@jeanpompeo2095]

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 :)

[@goyalsambhav2002]

Great explanation on the GLMs. It gave me some new insights for sure. May you keep growing! Thanks for the video. I guess I'm gonna land at your channel quite often :)

[@alexfranciosi9579]

Honestly the best content on KZbin

[@dataman1000]

this is true!!

[@ericpenarium]

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

[@IsaacJolayemi]

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

[@TheProblembaer2]

It’s so much fun and informative to listen to you. And you were are talking about general linear models.

[@tomaswust3505]

Extremely helpful video ! Thank you for your clear explanations

[@ProjectNomad]

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

[@James-l5s7k]

Finally, a channel that speaks sensibly!

[@QuantPsych]

We're an endangered species.

[@emilioalfaro4365]

Amazing video, just understood GLM's, of course after not understanding with books and web pages. I was assigned to teach this topic in class and you just saved the day. Thank you Dustin!

[@galenseilis5971]

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.

[@FroggyJumps747]

Very straightforward explanation of the link function! Thank you

[@QuantPsych]

Glad it was helpful!

[@janak5147]

Thank you, I loved this, I was smiling during the whole video and - most importantly - understood what generalized linear models are about!

[@galenseilis5971]

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.

[@galacticnose]

This is the most helpful video I've ever found

[@keerthanavivin450]

Thanks so much for these videos! You're an amazing teacher.

[@mahmudaislam5428]

Love love your presentation. Good way to engage people

[@QuantPsych]

Thanks!

[@monygham1344]

Great explanation, it put so many things I had in mind in the right order. Sub. Thank you!

[@ahmadbakraa2524]

Your work is appreciated, Thank you very much!!

[@Tascioni49]

This is what I always need, someone explaining things with some fun and at the same time in dummie terms xd

[@mohamadrezabidgoli8102]

Great video. One remark: At 9:55 the link function of linear regression is not 1, it is identity function f(x) = x

[@rohanchess8332]

This is was very nice, had a nice laugh but very educational too, lmao

[@gabrielbrandao9857]

Guy! You're amazing. Good job!

[@jekamito]

your videos are brilliant, thank you so much

[@milenaoliveira2626]

Amazing hahaha it helped me more than I expected. Thanks

[@edwinjesuspaleta9022]

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.

[@nkengfuasamuel1755]

I really enjoy your language style of teaching 😂😂😂❤

[@ndilzy]

Wow. Fun. Thanks learned a lot without getting bored

[@QuantPsych]

Glad you enjoyed it!

[@bchaitu]

Alright, let me comment on your video! The moment I started the video, the first few seconds I thought I wouldn't be able to make it to the end of the video, may be because the way you spoke (its not your problem, but mine. I am little too sensitive and can't bear loud noise. My sincere apology for writing this) BUT, after a minute, my brain started enjoying it because of the simplicity in your explanation, your deep knowledge of the subject and your power to connect with your students (people watching this video). I am so grateful to you 🙏😊 (subscribed, clicked on the bell icon, and going to be regular visitor to your channel 😄)

[@cofi9659]

Really great video, thanks

[@raltonkistnasamy6599]

Man u are an amazing teacher

[@normandaurelle814]

Thank you for your work, your videos are great. :)

[@Qwertyuio3165]

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

[@dsavkay]

Amazing video thanks

[@haidar2636]

amazing vid, thank you so much, subscribed

[@vicentemaass4810]

Very clearly explained!! Thank you sir

[@clarabuchholtz6707]

Thank you so much for your videos- I'm so grateful for the explanations, and feel they've been clarifying sticking points for me left and right! Question: A sticking point I'm still struggling through is the relationship between the shape of your data, the shape of your residuals, and what this means for your choices in building a GLM. 1- You mention that if your data isn't normal, you should use a GLM. If it's the residuals that really matter here, is that because if your data isn't normal your residuals are likely also not normal? 2. following up on the above- if your data are not normal, but your residuals are normal- does that mean you can just proceed with the model you've got as is? Or might you still run into problems? 3. Are normal residuals a sign of you having a decent model fit? So if they aren't normal, this is a sign you should use a GLM...for a better fit? And when having done so...do your residuals hopefully become normal as a result? In other words- does a GLM "fix" your model to give you normal residuals -or- does a GLM handle non-normal residuals such that it gives accurate estimates of for e.g. "95% confidence" for a non-normal distribution that fits your residuals? Hope those questions even make sense, and thank you so much again!!! I teach and know how much work it takes to put together things like this and answer so many questions- grateful for your time!

[@don-yin]

I cannot believe that you have only 3.7 k subscribers.

[@ALI_B]

Great stuff as usual. Keep up.

[@QuantPsych]

Thanks!

[@mikhaeldito]

Great video! May I suggest that a short blog post to summarise this content will be very helpful as well!

[@navjotsingh2251]

I love your craziness, and you are doing us a great service. Going forward, I’m going to scream “Generalised Linear Model!!!” At people who need it. Can you do a full course on GLM, the math behind it and I guess any other regression analysis theory. I think that would be awesome, or if you have already done this I couldn’t find it 🙁

[@QuantPsych]

I have a couple playlists related to what you're asking for. I tend not to get mathy (because it scares my students :))

[@briankron1377]

Quick question for you, if you're still checking these comments! When taking the next step and moving up to GLMMs because of the requirements of data structure, is it a necessity to still use a link function in your code? Thanks, love your videos

[@mathisdifficult666]

难以置信的好视频！我能够感觉到他是真的懂

[@КостадинКостадинов-ц8е]

Thanks you and I’m waiting for gamma distribution example will be useful in my resurch

[@오신근-y8k]

What an interesting host who are full of statistics.

[@indrafirmansyah4299]

Thank you for the video! The explanation is clear.

[@galenseilis5971]

The residuals from the conditional mean from a gamma generalized linear model will not be gamma-distributed. A quick way to confirm this is to realize that the outcome variable is sometimes less than the predicted mean value, resulting in a negative residual. But a gamma distribution has non-negative support, and therefore cannot be the distribution of the residuals. In general the residuals do not follow the same distribution as the likelihood.

[@yogeshpahari589]

Thank you from Nepal

[@alejandrovillalobos1678]

thank you so much for your videos, greeting froms mexico

[@paulyoung3897]

This was great

[@yashagrahari]

First 100K views. Congrats! Keep it on.

[@QuantPsych]

Thanks!

[@adammickiewicz7818]

You're a legend, thanks a lot

[@aun3931]

You first spoke of data being normally distributed and then residuals being normally distributed. Could you please distinguish between the two?

[@alexhan3390]

this was amazing! thank you :)

[@radiancewithjasmin]

This was so great, thanks!!

[@sheeta2726]

Great Video!!!!

[@justinmiller4406]

I was surprised at how complex problems can be solved with a simple two-layer feedforward binary classification neural network. With a single hidden layer with a ReLU activation function, followed by an output layer with a sigmoid activation, it is able to learn very complex binary classifications (Such as learning financial signals). Unfortunately, I did not see any tutorials on financial data modeling using linear layers - most are using CNN, LSTM, and GRU model types. Those model types just don't seem to learn my dataset as well as this two-layer feedforward binary classification neural network does. Fun topic!

[@Ifly44]

Really well explained

[@patriciasobirin8210]

very concise video very. concise

[@galenseilis5971]

The video plots a density for a Poisson distribution, but a Poisson distribution is discrete. Thus such a density plot is just a rough approximation of the probability mass function of a Poisson distribution.

[@qwerty11111122]

The plot is kinked, so it is discrete. But he def should have made a histogram instead

[@galenseilis5971]

@@qwerty11111122 I might not be understanding what you mean by "kink". If by "kink" we mean a discontinuity, then you should consider the counterexample found in the Laplace distribution. The density function of a Laplace distribution is non-smooth at its mode, which also for this distribution equals the median and mean. Even though it isn't smooth everywhere (it has a "kink"), is it not a discrete probability distribution. Fortunately a weak derivative exists at this point even though ordinary derivatives do not, so many of the same results can be obtained almost-surely (i.e. up to a set of measure zero).

[@jonathanevans4817]

Thank you, this is excellent. I did find the music distracting, however. :)

[@qwerty11111122]

Rowan University! I was in the first year of freshman to go all 4 years majoring in bioinformatics!! Edit: negative binomial mentioned 15:15

[@QuantPsych]

A fellow prof!

[@tereseteoh2154]

i love this video so much

[@mrQueppet]

Bravo, sir.

[@NM-tx7zm]

Thank you!

[@Tobster627]

So say I had one predictor variable, weeks, and one dependent variable, counts. When I plot x vs y there is a clear quadratic relationship. So should I use a sqrt link function in the poisson or negative binomial model that I end up running?

[@QuantPsych]

Makes sense to me. Maybe try both the log link and a sqrt link and see if it actually fits better.

[@crushed_oreos]

Thanks a lot man

[@kiwanukajoseph6812]

So can we conclude that "tobit models, truncated models, and the heckmann model( tobit II model) follow a Gamma distribution?

[@neneirene7961]

i love this teacher

[@dragcot9677]

as an ecologist in progrees I can say, in ecology EVERYONE is using GLM all the time even when they could be using other simpler methods so here I am trying to actually understand them ahjhahaha

[@QuantPsych]

Ha! Sounds like you're better off in ecology than here in psych.

[@FrederickWagenknecht]

Heyy, thank you for your great video!! I have a question on the difference between transformations and link functions. Is it right that this shouldn't be the same? mean(log(y)) log(mean(y)) And this should be the same? mean(log(predict(mod))) log(mean(predict(mod))) If yes, why is this the case? Thank you a lot!

[@theuser810]

In 12:10 it says log, but the systematic components seem to be exponentiated. Which one is correct?

[@RomainPuech]

The link function is applied to y, so you get f(y) = systematic component, that why you apply your systematic component to the inverse of the link function. Note that for id and 1/x, the link function is its own inverse that's why you only spotted it for Poisson

[@theuser810]

​@@RomainPuech Thanks, I got it now!

[@marianolan1550]

Great video I have a question about the inverse link 1/x if you use the R default for Gamma. Is it right that interpreting the coefficients you switch the relationship so if the coefficient is -0.8 this is actually a positive relationship not negative?

[@QuantPsych]

Correct.

[@goelnikhils]

Good Video

[@peachorchard]

Omg! I wish I was in your class

[@davidireland1766]

What happens if you have a mixture of variable types. Continous, discrete etc.

[@QuantPsych]

Fit a mixture model. I haven't used them often, except for zero-inflated models.

[@nachete34]

Thank you for the video and all the work behind! You really made a complicated topic (at least in my head) look very easy. Two questions I'd appreciate if you could reply: 1. When checking whether to use gaussian or gamma GLMM, should I check distributions of the original data or of the residuals? (I often see people checking the original data while it is often said we should check the residuals) 2. Can I blindly trust AIC or BIC to quickly determine whether to use gaussian or gamma GLMM? i.e., without needing to plot the data. Thanks in advance!

[@QuantPsych]

1- You are right. We look at the *residuals*. 2-I wouldn't trust anything without plotting the data :)

[@jg95095]

@@QuantPsych To clarify #1, is that the residuals of a linear regression fit?

[@skyscraper5910]

How does one actually test for significance with these models?

[@cedwin4]

How about inverse binomial and tweedie distribution? Can you make a video?

[@anurudhyak2904]

Thank you very much for the vide. It's very helpful. However I have few questions. 1. How do I find out if my data follows gaussian or gamma? I did Shapiro Wilk test to check for normality and it is not normal. But I am not sure if they follow gamma distribution. 2. How does the prediction change based on the family and link function? Suppose I have the same gamma distribution but have different link functions, how will it affect the model fitness? Or rather how can I choose the link function? 3. Is there any method to check the goodness of fit?

[@luisvasquez5015]

Are link functions a special case of activation functions (in the context of NNs)?

[@varotama2980]

its a great video, thank you. but can i ask you some question, if i use poisson with 2 predictors, can i make it into plot diagram? sorry for my bad english, im from indonesia

[@QuantPsych]

With flexplot you can.

[@idontevenwanttomakea]

AMAZING!

[@maddisonbrown9513]

Not me giggling about your "Poisson" pronunciation in my office. Didn't know GLMs could be so funny.

[@SidneyRanger1138]

Thank you so much!

[@nadaelnokaly4950]

can we just rebel all over the worlds and shout out: "we need our teachers/professors to be LIKE THISSSSSSS!!!!!!". we need instructors who make things make sense to us, not a parrot that re-read the textbooks/slides!

[@dinandbakker7805]

I’m afraid that this mostly works for other people with ADHD

[@cabbages3424]

So if I have only one poisson distributed independent variable and one poisson distributed dependent variable, they have a linear relationship, should I be using 'poisson' distribution as the random component, and 'identity' as my link function? In MATLAB: glmfit(x, y, 'poisson', 'link', 'identity');

[@yasithudawatte8924]

Thank you. Clear explanation. Can we use GLM when observations are dependent or correlated? Or is it a situation where GLMs not applicable?

[@QuantPsych]

You cannot. You'll have to use mixed models (or time-series models).