Linear models make assumptions (homoskedasticity, linearity, independence, and normality). How do you evaluate them? Well, check it out, dude.
Learning objectives:
understand what a residual is (both graphically and mathematically)
components of a model (fit and error/residual)
know what a residual tells you about your model
the four critical assumptions of linear models
normality of variables versus normality of residuals
homo (or hetero) skedasticity — what does it mean?
what does independence mean?
why do we assess assumptions?
how to interpret a residual dependence plot (and what it tells you)
how to interpret an SL plot (and what it tells you)
how to model data that violate these assumptions:
https://www.youtube.com/watch?v=BwH945JdD90&t=50s