Assumptions of Linear Regression

Assumptions of Linear regression:
Linearity, No Multicollinearity, Homoscedasticity, Normality of Residual

[@TheNachosvastav]

Linearity does not mean x and y should be linear it means that the equation or the Betas or the co-edficients should be additive so y=M1 + m2 X2 + M3 log(x3) +e still confirms to linear assumptions

[@duasanam9150]

Thank you very much sir for such a
great explanation. Highly appreciable work.

[@imran_kohli]

Thanks a lot Brother for b'ful description and the way u describe.... really amazing 👌🏻👍🏻

[@shahinmia7377]

I am very clear about assumption of linear regression. Thank you so much sir.

[@saitarun6562]

thanks alot explained very clearly so good one

[@chalmerilexus2072]

Informative one

[@SavitaDevi-qv8qq]

good explaination sir tnx

[@armyman9907]

In the lower bottom right, you can't say their is no relationship. Here relationship present but no linear relationship. This type of relationship called curvilinear relationship.

[@mehedihsust]

well explained

[@uzmasofthome]

excellent, please make more videos

[@urmisatra8808]

Thank you for the video

[@rohitbhosale4614]

Thanks

[@baravind6548]

first you get clarity on what you want to say.. later u can explain.. waste of time ntg is clear.

[@SanjeevKumar-nc2rt]

Hello friends