  Linear regression is a classical paradigm in statistics. A new look at it is
provided via the lens of universal learning. In applying universal learning to
linear regression the hypotheses class represents the label $y\in {\cal R}$ as
a linear combination of the feature vector $x^T\theta$ where $x\in {\cal R}^M$,
within a Gaussian error. The Predictive Normalized Maximum Likelihood (pNML)
solution for universal learning of individual data can be expressed
analytically in this case, as well as its associated learnability measure.
Interestingly, the situation where the number of parameters $M$ may even be
larger than the number of training samples $N$ can be examined. As expected, in
this case learnability cannot be attained in every situation; nevertheless, if
the test vector resides mostly in a subspace spanned by the eigenvectors
associated with the large eigenvalues of the empirical correlation matrix of
the training data, linear regression can generalize despite the fact that it
uses an ``over-parametrized'' model. We demonstrate the results with a
simulation of fitting a polynomial to data with a possibly large polynomial
degree.
