What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression

[@msgupta07]

Timeline of Assumption of linear regression 0) 00:51 Introduction 1) 01:58 Linear relationship (between all the independent and dependent features) 2) 04:25 No multicollinearity (between independent features) 3) 11:37 Normality of residuals (Distribution of residuals should be normal) 4) 13:15 Homoscadascity (residuals and predicted values should not have any pattern ) 5) 15:32 No autocorrelation of residuals

[@tanjulgohar5]

Sir aapse accha koi nhi samjha sakta ❤️

[@kunikakhobragade6953]

Sir aapne bohot hi badhiya padhaya ....after watching a lot of videos but not founded a video like this ....now i understood the concept only by you ...ty

[@laxminarayangaidhane7116]

Sir if possible make video on AUC ROC curve...and thank you for making this video

[@shahidafreedy]

I truly appreciate your explanation; it's been incredibly helpful. Thank you very much, sir!

[@anujsinghkushwah2712]

thanku bhai the most "to the point" and easiest explained video in youtube.

[@prateeksrivas89]

Very Helpful Video for people who grasp a concept from fundamentals. Very intuitive with practical implementation.

[@jashneetkaur3176]

Yours videos are very well explained ,Thank you soo much Sir for giving the knowledge. You are the best teacher ever

[@lothalopolis]

1:44 During train_test_split, the rows of the data are randomly ordered (unless you set a parameter not to reorder, which is not set here). Because of this, the residuals at 16:08 will always show no auto-correlation even if it was, as the order is jumbled up.

[@shreepalpawar9437]

Sir,ur teaching skills is very awesome ❤️It was much helpful for me Thanku 💐🎉

[@rajshekharrakshit9058]

This is what content is. I hope you will give deep understanding on other topics too

[@ishuraj7407]

The only video on YT that explains assumptions of alogorithm. Thank you so much sir this video was a great help. Sir, can you please make videos like this for other alogorithms also.

[@deepakalur5603]

This Question has been asked in Turing data scientiest interview Sir, thank you so much.

[@aravindhgowtham8200]

I am not sure if multicollinearity is a strict assumption. Multicollinearity does not have any effect on the predicitons, it only effects your coefficients. So, if inference (interpreting coefficients) is important to you, then multicollinearity is an issue. If not, it is not big of an issue. Correct me if I am wrong.

[@NidhiSingh-2411]

One of the best and quick video I have seen

[@unitedpakistan8516]

Thank You Sir, the way you make us understand is really great... Love From Pakistan 💖

[@vikaskadam9842]

Great explanation sir,simple illustrated by example

[@NishaSharma-se1js]

Thank you for this wonderful information🎉

[@divyanshusharma4576]

Hi Nitish there is one more assumption for this our response variable should be normally distributed please explain thats why we use GLM

[@sachin2725]

Dude, you are really a genius......excellent explaination

[@kumuddandale158]

in first assumption of linearity how will you check linearity of categorical predictor with target

[@biswasshubendu4]

ON THE POINT!!!!! VERY IMPORTANT INFORMATION REGARDING INTERVIEWS

[@arfapathan1832]

you simplified the concept..Thank youuuuu

[@ParthivShah]

Amazing Knowledge Sir.

[@nikhilbansal855]

In both assumptions 3 (normal residual) and 5 (autocorrelation) , we are plotting residuals, How come assumption 3 says it is normally distributed but 5 says there is not relation?

[@learnwithajmal8829]

sir videos is very good , sir we need a videos for that case if assumption not satisfy how we can use remedy of these assumption in python

[@nilkantgudpale1959]

thank you clearly explained the concepts

[@sourabhagarwal4852]

Good Video on Assumptions of Linear Regression🙂

[@DataScienceWithAkesh]

Sir i am facing a bimodal residual issue or problem dont know what to say. Even my teacher dont helped me in that. Can you give some points or anything

[@619vijay]

Thank you. Very helpful

[@rachitsingh4913]

Hello Sir, As always this video is also amazing no doubt about that. But in this video you only explained How to check the assumptions. But what if the assumption is not hold than how to tackle them ?? Like what are the processes in order to convert the data so that it holds all assumptions. please make video on that and explain that.. Thankyou Sir

[@because2022]

Very nice explanation❤

[@shalinigoud802]

Thanx for clear explanation it was quite informative

[@srkandekar]

Thankyou Sir. I will reference this content.

[@ge5850]

Its very very good lecture sir thanks a lot

[@datamatrix20]

Please make video on how to overcome each assumption if it is invalid

[@abhaykumaramanofficial]

Thanks you for simple and great explanation

[@ajaychinni3148]

The only missing thing was the "why" Why do we need these assumptions of linear regression. You only explained Multicollinearity would have been perfect if explained for all.

[@monalishasahu1276]

Very well sir, thank you so much 😊

[@xploramit]

Hi sir, The 1st assumption of linear regression is that the equation should be linear in parameters and there is no restriction on how x and y are related. But u showed in ur video that if there is non linear relationship between x and y then the equation doesn't holds the assumption which I think is not right.

[@shahilgourisaria2336]

Very nice explanation sir

[@ParthivShah]

Thank You Sir.

[@balrajprajesh6473]

Best teacher ever!

[@shivarajnavalba5042]

great explanation... thank you! 😇

[@sumjakar]

Nice video sir Thank You So Much

[@anirbansen9285]

Excellent content Sir but I have a doubt. Residual should be bell-shaped then how it's not holding any auto-relation correlation?

[@Keep_Laughfing]

Same Question asked me. Can you explain all assumption of all algorithms?? Plz sir that will be very helpful for us. 🙏🙏🙏🙏

[@ashvinibhuskade6250]

Nice video sir.., the no autocorrealtion assumption is only for linear regression or it is applicable for other algorithms

[@nikhilgupta4859]

Sir apne btaya how to check linearity, but ye nahi btaya agar non linear h to krna kya h

[@diwakargupta0]

Sir in Autocorrelation of Residuals if we sort the data then it will also follow some pattern. This plot depends on the order of input and we can pass input in any order. btw great video. Thanks

[@sumansamantaray4886]

Sir, aapne bataya tha January me NLP ka playlist khatam hoga ! Abhi June khatam hone wala hai 😞

[@shortflicks83]

Great video

[@namanmodi7536]

deep learning video sir!

[@harshithasuri663]

Conceptually what does auto correlation of residuals represent? You explained nicely why there should not be a correlation b/w Independent variables. But I didn't understand significance of no-auto correlation assumption for residuals

[@vijaylaxmilendale3399]

1. Linear relationship between input and output 2. No multi collinearity 3. Normality of residual 4. Homoscedasticity 5. No auto correlation in residual

[@pankajbhatt8315]

Nice explanation

[@yagnikposhiya7019]

Great Explanation.. But can you please make a video on detail explanation of autocorrelation and homoscedasticity??.. Thank You

[@Compact18]

What if these assumptions get violated ?

[@pratikghodke7983]

good one sir

[@jyotsanagour850]

Well Explained

[@shadiyapp5552]

Thank you sir ♥️

[@harshmankodiya9397]

hello there. As u said these are the assumptions in LR and a candidate who is not aware of these is judged. But the thing is, from where can one read about such concepts?. Can you please suggest some books with solid ml fundamentals as there is a lot of ambiguity about concepts in ML books and not every book talks in depth about these algorithms.

[@campusx-official]

www.amazon.in/Elements-Statistical-Learning-Prediction-Statistics-ebook/dp/B00475AS2E

[@debjitsarkar2651]

SIR how can I join your online 6 month Ml &AI COURSE?PLEASE REPLY SIR.Thank you🙏🏻

[@reshubathla8138]

Very nice ...

[@ShubhamVerma-wf3vc]

Thanks jitu bhaiya.

[@vivekpawar1854]

Sir how to handle multicollanirity??? should we drop one column???

[@tanmay_efootball]

yes , we have to drop (highly i think )

[@ritujawale10]

Thnks you sir... 👍

[@ishandandekar1808]

Sir please keep making Deep learning videos for the 100 days ml playlist

[@SaranRavali]

It would have been a good video , if the reasons behind these assumptions is well explained. the reasons behind the Normal Residual, Homoscedasticity, No Autocorrelation of Error are not explained. how does these assumptions impact the model is not explained. Thanks for explaining the meanings of these errors with examples.

[@gauravsharma-sd2mg]

Awesome 👏

[@shrinathjagtap6703]

Make video on What to do if these assumptions get violated

[@ashutoshthokare2127]

Thank u sir

[@krishnab6444]

NIcely Explained

[@debatradas1597]

thank you so much sir

[@thethreemusketeers4500]

sir plz Deep Learning and NLP ki playlist complete kr do.

[@viralvideoa]

Thankyou sir 🙇

[@teenagepanda8972]

Thank you sir

[@navtojsingh]

bravo!

[@partharora6023]

amazing sir

[@rafibasha4145]

Thanks bro

[@rahulaher3874]

thank you sir , apne iski githhub link di hoti to time save hota hamhara...

[@Vipulghadi]

thanks sir

[@ParasProgramming123]

Can you make tutorial on deep learning

[@campusx-official]

100 Days of Deep Learning: kzbin.info/aero/PLKnIA16_RmvYuZauWaPlRTC54KxSNLtNn

[@ParasProgramming123]

@@campusx-official thank you sir. Sir is macbook air m1 good for such work such as ml and dl I am watching your 100 days of machine learning and reached day 3 because it been only 3 days of me starting this new journey

[@ninderjoshi7384]

I would suggest you to explain why linear regression assumes, normal residuals, homoscdacitiy and no correlation between/w residuals/independent variables. That will help make your channel different from others because it will help your audience understand the concept better. The things you have explained, anyone can explain it, but only a handful number of people explains "why"

[@arun5351]

You can explain the reasoning behind it. Others can also chip in. Nitish can correct our understanding if there are any gaps.

[@iftikhar58]

Thank Men

[@vishnupsharma50]

You should actually rectify yourself. The linear assumption is never about straight line... it is linear in estimation parameters. y= k x^2 is also linear regression. Please correct.

[@jams6279]

🎉🎉🎉

[@tanmaythaker2905]

LR No multicollinearity Normality of residuals Error should have constant variance No auto correlation of errors

[@rajatchauhan4410]

but why these assumptions??

[@DarkLord79799]

nice

[@Dyslexic_Neuron]

Wasted 19 minutes . You should explain the reason for having these assumptions

[@surajvishwakarma4534]

🤡🤡

[@pratiknaikwade95]

agar result 1 se 5 ke bitch mai aarahe ho to "multicolinearity hai ya nahi"???????🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🤨🙄🙄🙄🙄🙄🙄🙄🙄🙄

[@arpanpal9860]

Thank you sir❤❤