Principal Component Analysis (PCA) is a statistical technique that reduces high-dimensional data with many variables into a smaller number of principal components (composite variables) while minimizing information loss. It transforms correlated variables into uncorrelated components, making it easier to understand the underlying structure of the data. PCA is widely used in marketing, quality control, and other multivariate data analysis contexts.

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• 2023r05 #6A社では，顧客の行動や天候，販売店のロケーションなどの多くの項目から成るデータを取得している。これらのデータを分析することによって販売数量の変化を説明することを…