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Multivariate data analysis focuses on understanding the relationships, dependencies and patterns among the variables. Techniques employed in multivariate data analysis include multivariate regression analysis, Principal Component Analysis (PCA), factor analysis, discriminant analysis, cluster analysis and canonical correlation analysis.

High-dimensional data refers to datasets with a large number of variables or features relative to the number of observations. This scenario presents unique challenges, such as the curse of dimensionality and sparsity. High-dimensional data analysis aims to uncover relevant structures, relationships and patterns in these datasets. Techniques used in this context include variable selection methods (e.g., LASSO, ridge regression), dimensionality reduction techniques (e.g., principal component analysis, sparse principal component analysis) and clustering techniques tailored for high-dimensional data.

Multivariate and high-dimensional data analysis is crucial in understanding complex systems, making predictions and extracting meaningful insights from data. These techniques help researchers and practitioners uncover hidden patterns, identify important variables, reduce data dimensionality and develop models that capture the underlying structure of the data.

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