[SubiculumCode]

In my work, I hardly ever use linear regression, but do use multiple linear regression. Multiple linear regression allows multiple linear predictors, where the method parses shared and independent variances associated with each predictor. These discussions on linear regression hardly ever touches on the very useful multiple linear regression method. In the case of bad variance inflation in models with multi-collinear predictors, robust regression techniques are advised like ridge, LASSO, or elastic net regression.

In relation to gradient descent, I do not know enough if multiple regression is at all relevant, or why not.

And yeah, for non-normal error distributions, we should be looking at generalized linear models, which allows one to specify other distributions that might better fit the data.