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Multiple regression. How to deal with Outliers and Colliniarity 

R Programming 101
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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).

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10 мар 2024

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Комментарии : 8   
@davidsanjenis2778
@davidsanjenis2778 4 месяца назад
Great series! I'm really looking forward to following the rest of it! :D
@insanity_gaming2927
@insanity_gaming2927 4 месяца назад
*We expect "Machine Learning using R" in the next lessons, pleaseeeee*
@insanity_gaming2927
@insanity_gaming2927 4 месяца назад
Amazing👍
@roderickmorley4808
@roderickmorley4808 3 месяца назад
Greg, the vide on Effect Modifiers and Interactions is missing from the playlist ?
@mansamusa2275
@mansamusa2275 4 месяца назад
Hello hope your doing well man. How do I navigate your website in order to get the pdf for today's lesson?
@davidsanjenis2778
@davidsanjenis2778 4 месяца назад
i think you have to pay to access it.
@uma9183
@uma9183 3 месяца назад
provide scipt also sir ???
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