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.

Is normality required for regression?

Regression only assumes normality for the outcome variable. Non-normality in the predictors MAY create a nonlinear relationship between them and the y, but that is a separate issue. ... The fit does not require normality.

Can you use linear regression if data is not normally distributed?

In short, when a dependent variable is not distributed normally, linear regression remains a statistically sound technique in studies of large sample sizes. Figure 2 provides appropriate sample sizes (i.e., >3000) where linear regression techniques still can be used even if normality assumption is violated.