Why do we need to check the normality assumption in target variable?
The normality assumption for linear regression applies to the errors, not the outcome variable. It is assumed that errors are independent and identically distributed with mean = 0 and some variance. Non-normality of the errors will have some impact on the precise p-values of the tests on coefficients. But if the distribution is not too grossly non-normal, the tests will still provide good approximations.