Multiple regression. How to deal with Outliers and Colliniarity

When doing linear regression or multiple regression, your data may have outliers. Outliers are data points where the residual values are far from the model. In this video we explore how to identify outliers and discuss what to do when they are found. Colliniarity or multicolliniarity occurs when two or more of the explanatory variables are correlated. There are times when these variables should be kept in your model (when confounding is suspected for example).

[@davidsanjenis2778]

Great series! I'm really looking forward to following the rest of it! :D

[@insanity_gaming2927]

Amazing👍

[@insanity_gaming2927]

*We expect "Machine Learning using R" in the next lessons, pleaseeeee*

[@roderickmorley4808]

Greg, the vide on Effect Modifiers and Interactions is missing from the playlist ?

[@mansamusa2275]

Hello hope your doing well man. How do I navigate your website in order to get the pdf for today's lesson?

[@davidsanjenis2778]

i think you have to pay to access it.

[@uma9183]

provide scipt also sir ???