Below, I show some generated data for a linear regression. With these features (also known as variables, covariates or predictors) `A, B, C, D`

and `E`

, I aim to predict an outcome `Y`

. Would the data shown below be considered multicollinear in the sense that it could become problematic for linear regressions?

I would say yes, and I read that Bayesian models can handle collinear data well, so I expected a huge difference between a Bayesian and Frequentist model. However, I compared a Bayesian to a Frequentist model and they gave the same outcomes, see the figures below. Therefore, I concluded that the Frequentist model did not have any issues with the collinearity.

Might this be because `lm`

from GLM uses QR decomposition? Or, is my data not correlated enough? When would `GLM.lm`

start showing huge variances as mentioned in Wasserman’s lecture notes?