Ideological and Political Exploration for Multivariate Statistical Analysis Methods

Statistics is an important component of data science, and multivariate statistical analysis methods play a crucial role in analyzing high-dimensional data and extracting effective information from it in the era of big data. Under the wave of domestic curriculum ideological and political education, this paper explores the organic integrations of multivariate statistical analysis methods such as regression analysis, variance analysis, factor analysis, principal component analysis, canonical correlation analysis, k-nearest neighbor algorithm, and hierarchical clustering with ideological and political education. The study makes analogies between the principles, ideas, advantages, and applications of these methods and some ideological and political contents, making abstract multivariate statistical analysis methods more vivid and helping students establish correct worldviews, outlooks on life, and values.