Full course is now available on my private website. Become a member and get full access: meerkatstatistics.com/courses... * 🎉 Special KZbin 60% Discount on Yearly Plan - valid for the 1st 100 subscribers; Voucher code: First100 🎉 * “GLM in R” Course Outline: Administration * Administration Up to Scratch * Notebook - Introduction * Notebook - Linear Models * Notebook - Intro to R Intro to GLM’s * Linear Models vs. Generalized Linear Models * Least Squares vs. Maximum Likelihood * Saturated vs. Constrained Model * Link Functions Exponential Family * Definition and Examples * More Examples * Notebook - Exponential Family * Mean and Variance * Notebook - Mean-Variance Relationship Deviance * Deviance * Notebook - Deviance Likelihood Analysis * Likelihood Analysis * Numerical Solution * Notebook - GLM’s in R * Notebook - Fitting the GLM * Inference Code Examples: * Notebook - Binary/Binomial Regression * Notebook - Poisson & Negative Binomial Regression * Notebook - Gamma & Inverse Gaussian Regression Advanced Topics: * Quasi-Likelihood * Generalized Estimating Equations (GEE) * Mixed Models (GLMM) Why become a member? * All video content * Extra material (notebooks) * Access to code and notes * Community Discussion * No Ads * Support the Creator ❤
@bfod Жыл бұрын
I tried to sign up but it wouldn't work
@user-or7ji5hv8y3 жыл бұрын
This is the best high level explanation yet to understand the motivation.
@gloria96793 жыл бұрын
omg, finally i found short and clear video , thank u !
@메호대전6 ай бұрын
It is the best lecture that I have watched on KZbin. Thanks.
@sarkersunzidmahmud28752 жыл бұрын
thanks a lot for the explanation. But I was thinking that in the linear model, Normally we use Y as the response variable and X as the independent variable, where response Y is dependent on X. That's why I got a little bit confused at first when u are taking Y as the observations.
@tullee72283 жыл бұрын
Independent variable Y doesn’t need to be Normally distributed, it just need to be a from distribution from Exponential Family. The only assumed Normality is the Residual
@MeerkatStatistics3 жыл бұрын
In linear models, y is normal. In GLM's y can be any exponential family.
@浣熊X3 жыл бұрын
@@MeerkatStatistics y should be normal given x
@keerthanavivin4502 жыл бұрын
Great video. Just the explanation I was looking for!
@theforester_2 жыл бұрын
wow! thanks very much! big shout out from brazil
@petragonzalez78682 жыл бұрын
This was awesome dude! Thanks for that, really!
@MeerkatStatistics2 жыл бұрын
Warms my heart :-)
@杨凇-y3b2 жыл бұрын
Thanks, I love this video so much
@anthonywashington2885 Жыл бұрын
YOU ARE AWESOME
@romgossel79712 жыл бұрын
Hi, great video 👍🏻 Just a comment/question. Perhaps am I wrong, but in my understanding, the normality of residuals (or of y's if you prefer) is not formally required for the parameter estimation for the best fit line using the ordinary least square methods (linear model). The Gauss-Markov theorem requires other assumptions about the errors (such as finite variance / homoscedasticity or zero conditional mean...) to ensure that the OLS gives the best linear estimator... but normality itself is mostly important for inference (drawing confidence intervals), not for parameter estimate. In other words, even in violation of normality, we cannot conclude that the OLS would not give the best linear unbiased estimator. As I said, perhaps am I wrong.
@MeerkatStatistics2 жыл бұрын
No, I think you are right. There are some properties that can be achieved by simply demanding homoscedasticity or zero conditional mean. But for more properties you will need also the normal assumption. In Agresti's book (Foundation of Linear and Generalized Linear models) chapter 2 is devoted to Linear models without the assumption of normality, and chapter 3 is devoted to "Normal Linear Models". You should check it out.
@romgossel79712 жыл бұрын
@@MeerkatStatistics Thanks for the answer and for the tip, I'll have a look indeed :)
@factsfigures27403 жыл бұрын
very well explained
@TiagoPereira-hm1nq3 жыл бұрын
Fantastic! Bravo!
@mdevdatta Жыл бұрын
At 2:11 I don't understand what you mean be "the coefficients are made linear". If we have a term like beta_i^2, what prevents us from redefining beta_i^2 -> beta_i? The beta's are all c-numbers, right?
@MeerkatStatistics Жыл бұрын
Nothing prevents you. But if you have y=beta0+beta1*x1 + x2^beta2, that's a problem. Same if you have y=beta1*x1/(beta0+beta2*x2). The main point is that the function has to be linear w.r.t. to the inputs, but it's ok if the x's are some transformations of themselves (i.e. x^2, log x, exp x, sin x, etc.).
@joejitsuway9603 жыл бұрын
Very Clear. Thank you.
@marcoantoniorocha90772 жыл бұрын
Stupendous!
@rishikeshp78802 жыл бұрын
Hi! Awesome videos dude! I do have a few questions - Are all linear models, gaussian linear models ? The assumption that errors/residuals have to be normally distributed, does it hold true for both regular linear models as well as GLMs ? Why cannot be use LSE for GLMs ?
@Hasanahmed20132 жыл бұрын
Thank you. What's the difference between MLE and Least squares? Sorry for the stupid question.
@MeerkatStatistics2 жыл бұрын
See the next video in the series 🙂
@fangqimaggieguo6713 жыл бұрын
Thank you
@MisterDives3 жыл бұрын
I'm trying to wrap my head around your point about the second assumption here - I thought with regular linear models it was required that the _residuals_ be normally distributed, not the data points themselves, but then is it that the residuals in GLMs can be from non-normal distributions? (as long as they're in the exponential family)
@MeerkatStatistics3 жыл бұрын
y=bx+residual, i.e. a systematic part + a stochastic part. Hence if the residuals are normal, the y's are normal. Or you could say, the y's are normal because the noise is normal.
@tullee72283 жыл бұрын
@@MeerkatStatistics in this case, y can be uniformed and still maintaining Normal residual
@MeerkatStatistics3 жыл бұрын
@@tullee7228 not sure I understand what you mean. Uniform and Normal are two different distribution, and a random variable can't be both.
@arnbrandy Жыл бұрын
@@MeerkatStatistics I think I understand @Tul Lee comment. IIUC it is the same as my doubt here. Let me use a great example I saw somewhere else to clarify this: The house prices in a city are linearly related to its area (in square feet/meters). Now, suppose I observe the prices and areas of some houses, and I noticed that there are more or less the same number of houses (let us say, 50±3) in each decile of the observed prices and observed areas. My price data points are uniformly distributed, yet I still can use linear regression. So, normally distributed prices are not a prerequisite for having a linear model, am I right? What is a prerequisite is that the *residuals* (defined as R = Y-bX) are normally distributed, R ~ N(0, σ²). Did we interpret it wrong?
@MeerkatStatistics Жыл бұрын
@@arnbrandy If y is uniform, the residuals are uniform. In this model there is only 1 source of stochasticity which are the residuals. They can be either uniform or normal but not both. So yes, you are wrong. However - there is no real requirement for the distribution to be normal. As mentioned in another comment here, it's actually a subclass of linear models called Normal Linear Models, which make some results (CI for the coefficients, Prediction Intervals, etc.) easier to get (though you can get them using asymptotic theory if n is large enough). The only thing "required" is for the residuals to have 0 mean and constant independent variance (though even non constant and non independent variance can be dealt with using GLS). This is just an introduction to the topic which tries to simplify it in order for most people to grasp it. Like most simplifications, it does not capture the full complexity and subtleties of the topic.
@nightmareluffy57163 жыл бұрын
Thankss a lot...this was very helpful.😀
@raltonkistnasamy65995 ай бұрын
thanka man
@sara-ql1xs2 жыл бұрын
excelent, thank you
@MsKakashi20123 жыл бұрын
thank you!
@Mirabell973 жыл бұрын
Thanks, that's very helpful :)
@suzykhaled34913 жыл бұрын
good explanation, keep on
@vncsna3 жыл бұрын
Thanks!!
@moshitammmabotha89002 жыл бұрын
Why are the videos hidden? How can i get them??
@MeerkatStatistics2 жыл бұрын
They are now offered for paid members in my website: meerkatstatistics.com/courses/generalized-linear-models-glms/ I made a video explaining how to register kzbin.info/www/bejne/gIaxgGetfruipZI&ab_channel=MeerkatStatistics
@fade-touched3 жыл бұрын
thx!!
@cgdarwin3 жыл бұрын
this was great! what is the app you use for writing? is it a white board?
@MeerkatStatistics3 жыл бұрын
Yes, but I since moved to OneNote
@imrul663 жыл бұрын
Hi! Thanks for the video. Can you please explain (in comments or in a video) how this relates to GLS?
@MeerkatStatistics3 жыл бұрын
They are two different things. Check here stats.stackexchange.com/a/272562/117705
@nkristianschmidt Жыл бұрын
y does not need to be normally distributed
@prashant01043 жыл бұрын
Thank you so much! I have a question - how is the ‘generalized least squares’ and ‘general linear models’ categorized wrt. to these two, and what are their differences to these respectively?
@MeerkatStatistics2 жыл бұрын
GLS is a different concept - used in linear models (linear regression). It accounts for a residual covariance matrix which is not the identity (i.e., the assumption of homoscedasticity and independence are violated). I might do a video about it in the future.
@faroukbenmeslem2654 Жыл бұрын
The Xi are indépendante not yi
@anglonrx27544 ай бұрын
Gauss didn't invent the linear model; he just claimed to a decade after someone else had. The same is true for Gaussian elimination. Newton invented it, and then Gauss decided to name it after himself.
@sahil00942 жыл бұрын
Linear regression assumptions are wrong. -Every observation doesn’t need to be normal. Residuals need to follow a normal distribution
@MeerkatStatistics2 жыл бұрын
If the residual (epsilon) is normal, what does it mean about the observation (y)? When y=b*x + epsilon.
@hanshansi759726 күн бұрын
@@MeerkatStatistics This assumptions are for the residuals and not for the y_i. He is correct, there are no restriction or assumptions about the observed distribution itself.
@MeerkatStatistics26 күн бұрын
@@hanshansi7597 For "simple" (fixed covariates x) regression you can assume normality for the residuals, or you can assume it for the y's. Since in this case b and x are considered fixed, the only source of randomness for y is the epsilon. If you have random x's, then the story differs.
@keimoon84789 күн бұрын
I think they mixed up between values of y and the value of y_i given x_i