Quantitative Methods Questions
Principal component analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while retaining as much information as possible. It is a multivariate analysis method that transforms a set of correlated variables into a smaller set of uncorrelated variables called principal components. These components are linear combinations of the original variables and are ordered in terms of the amount of variance they explain in the data. PCA is commonly used for data exploration, visualization, and dimensionality reduction in various fields, including statistics, data science, and social sciences.