Yes, you should check normality of errors AFTER modeling. In linear regression, errors are assumed to follow a normal distribution with a mean of zero. ... In fact, linear regression analysis works well, even with non-normal errors.

Does linear regression require normally distributed data?

Linear regression by itself does not need the normal (gaussian) assumption, the estimators can be calculated (by linear least squares) without any need of such assumption, and makes perfect sense without it. ... In practice, of course, the normal distribution is at most a convenient fiction.

Why data should be normally distributed for linear regression?

The residuals deviate around a value of zero in linear regression (lower figure). It is these residuals that should be normally distributed. To examine whether the residuals are normally distributed, we can compare them to what would be expected.