What is the VC (Vapnik-Chervonenkis) dimension of linear regression, and how does it relate to the generalization ability of the model?
a) The VC dimension measures the model's capacity to fit a wide range of data patterns.
b) The VC dimension is irrelevant in linear regression analysis.
c) The VC dimension assesses the model's interpretability.
d) The VC dimension is only applicable to non-linear models.