There are several assumptions that we take before applying linear regression.
These include :
- Eliminating multicollinearity.
- Normalization of the variables
- Eliminating homoscedasticity that is the correlation between error terms and independent variables.
For the third point, how do we treat a data set that has homoscedasticity in it? And how significant a change do we observe by doing so in linear regression?