I have been following SDS podcasts for around 8 months now but got to youtube channel just like a month ago. It is awesome seeing how quick you are getting new subscribers. Keep going :)
Then whats about correlation and Collinearity or multicollinearity ... If you take the square of that variable, it will cause correlation with X. which will make the regression biased. Should it be ok to use demean X in this case?
Hi, Polynomial regression of degree p in one independent variable x is considered. Numerically large sample correlations between xα and xβ, α < β, α, β = 1, ..., p, may cause ill- conditioning in the matrix to be inverted in application of the method of least squares. These sample correlations are investigated. It is confirmed that centering of the independent variable to have zero sample mean removes nonessential ill-conditioning. If the sample values of x are placed symmetrically about their mean, the sample correlation between xα and xβ is reduced to zero by centering when α + β is odd, but may remain large when α + β is even. Some examples and recommendations are given.
Cheers mate, it was an amazing explanation. Bytheway can you please tell me what software/ tool did you use to make this presentation. I mean data point Drawing
''used to see how pandemics spread'' :')
:) , The moment I heard him say "pandemic" , I smiled.
@@fetch9143 can he tell when it will end?
@@fetch9143 LOL Me too
Hi, SuperDataScince.
For more clear understanding, please add some simple examples.
Thank a lot.
I have been following SDS podcasts for around 8 months now but got to youtube channel just like a month ago.
It is awesome seeing how quick you are getting new subscribers.
Keep going :)
Thank you for your warm feedback Lukasz!
Just came from your ‘Machine Learning A-Z course’ for understanding Polynomial regression more in depth. Your course is really life changing 🙌🏼.
0:24 - there is a mistake with Polynomial Regression Equation , x1 squared should be x2 squared.
Great Explanation by the way !!!
No
in multiple LR, I usually visualise (at least for 1/2 features) as independent dimensions. How do I understand same in case of polynomial LR?
What determines the degree?
Then whats about correlation and Collinearity or multicollinearity ... If you take the square of that variable, it will cause correlation with X. which will make the regression biased.
Should it be ok to use demean X in this case?
How do you find the second coefficient and so on?
Great course A-Z machine learning ... Keep going Kirill.
Thanks for tuning in Rahul!
Hmm, I don't think Polynomial regression is a linear regression; it is non-linear improvement of linear models.
So if polynomial are linear, then for autocorrelation test we can use Durbin Watson test right?
Can we calculate value of x, with y given? using polynomial regression equation?
Where do I find the 'Multiple Linear Regression' video? I searched for it but wasn't able to find it on YT.
Brandon Foltz
Your explanations are interresting
Thank you, Sagar!
so easy to understand, you should open a patreon page
Multiple Linear Regression when are we getting your views on it. ? Am clear with the Polynomial
Do you want Multiple Linear Regression tutorial?
We can make it!
Just ask here, in the comment section
How do we calculate correlation for polynomials...?
Hi,
Polynomial regression of degree p in one independent variable x is considered. Numerically large sample correlations between xα and xβ, α < β, α, β = 1, ..., p, may cause ill- conditioning in the matrix to be inverted in application of the method of least squares. These sample correlations are investigated. It is confirmed that centering of the independent variable to have zero sample mean removes nonessential ill-conditioning. If the sample values of x are placed symmetrically about their mean, the sample correlation between xα and xβ is reduced to zero by centering when α + β is odd, but may remain large when α + β is even. Some examples and recommendations are given.
Well done thanks
Super, thank you for the good explanation. Can you give an example of situation in health research where polynomial regression can be used
Cheers mate, it was an amazing explanation. Bytheway can you please tell me what software/ tool did you use to make this presentation. I mean data point Drawing
Hey Masum!
Just PowerPoint
thank you!, this video made my mind clearest
Thank you for this! Very helpful!
thank you I love you
thereis no example!! and the writing (polynomial) not (polinomial)😎
Thank you. Very useful.
Thanks for tuning in!
I see you say "data", not "data"
Oh My God!!!!!!!!!!! Did you already know? 2:20
😂😂😂
y have to title it smal definition
shit you made it easy to digest
So profs are not smart enough to explain this concept like him lol. Feels bad
art of visualization, shows 1 graph and explains nothing at all
voozhoo voozhoo voozhoo.
Aeeeeee))))
5 minutes to explain nothing.