I've read that there are various assumptions associated with a multiple linear regression model which you should check/validate before getting too excited about your model results.
One of these is the assumption of linearity. I get that you would plot the dependent variable against the independent variable and visually check for linearity, but is there a more scientific way to do this?
I have the two plots below. Looking at the first, I can see some linearity by removing the outliers. The second however, is much harder. I can *maybe* see something, but I'm not sure if this is my eyes playing tricks on me.
If I determine the second plot does not satisfy linearity, what do I do? Exclude the feature from the model?