  Popular methods for modeling data both labelled and unlabeled, multiple
regression and PCA has been used in research for a vast number of datasets. In
this investigation, we attempt to push the limits of these two methods by
running a fit on world development data, a set notorious for its complexity and
high dimensionality. We assess the robustness and numerical stability of both
methods using their matrix condition number and ability to capture variance in
the dataset. The result indicates poor performance from both methods from a
numerical standpoint, yet certain qualitative insights can still be captured.
