Interpreting Regression Coefficients in Linear Regression

[@asdfghjkl12904]

when I hear this opening "Hi There", I am pretty confident that I am gonna learn valuable information :) Thank you!

[@christopherdeboer1500]

This example of two interesting beta (ß) variables being SQM and # of bedrooms might also prove useful for exploring multicollinearity (or even perfect collinearity) since it is difficult to increase the number of bedrooms without, generally speaking, an increase in SQM. Naturally one can turn an office into a bedroom and thus not increase the SQM, however it seems that you can only do this for so long before running out of rooms and thus it seems likely that there would be some correlation. Also, perfect correlation could occur if all bedrooms were the same size and we assume former alternative rooms are not turned into bedrooms.

[@mehradghazanfaryan640]

I am surprisingly wondering how come there less like for this clean and concise interpretation of regression
people are becoming less willing to encourage this much great tutorials

[@sachkofretef]

multiple regression fits a hyperplane, so in the 2 var case we get a planed through the dots, not a line !!

[@danielklein5560]

wouldn`t it be a plane instead of a line in the 2 Parameter case?

[@thomasyoung468]

What program are you using for the writing?

[@merthrka2583]

great explanation

[@tristanmurray3962]

Thank you.

[@sixtysixx3933]

With first sentence I know this is not going to be an Indian speaker. My ears could finally relax and listen to proper English.

[@sixtysixx3933]

@@prafulmaka7710 This could be true if I wasn't Chinese, hahaha.

[@prafulmaka7710]

@@sixtysixx3933 Haha nice one I’ll give you the point

[@gamalieleweke5627]

@@sixtysixx3933 hahahahahaha......