In linear regression the assumption is that:
Y = (X)T.A + B + E
where, A and B are the parameters and E is the error term or Noise.
Wherever I read, the model assumes that the noise is normally distributed, with a constant variance.
What if the E ~ N(0, c^2) assumption is broken?
What if the variance is not constant? Why does the variance have to be constant?
What if E follows another distribution?
Could you show the impact of breaking these assumptions in terms of mathematical analysis?
Cheers!
