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.

中文翻译：

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多元回归和主成分分析在世界发展数据建模中的稳健性和数值稳定性研究

用于对标记和未标记数据、多元回归和 PCA 进行建模的流行方法已用于大量数据集的研究。在本次调查中，我们试图通过对世界发展数据进行拟合来突破这两种方法的限制，该数据集因其复杂性和高维度而臭名昭著。我们使用它们的矩阵条件数和捕获数据集中方差的能力来评估这两种方法的鲁棒性和数值稳定性。结果表明，从数值的角度来看，这两种方法的性能都很差，但仍然可以捕捉到某些定性的见解。