multivariate-analysis

What problem does it solve?

High-dimensional data with many correlated variables can be hard to interpret. This skill provides a structured approach to uncover latent structure and reduce complexity using multivariate methods such as PCA, factor analysis, clustering, and SEM.

Core Features & Use Cases

• Dimensionality reduction with PCA to simplify datasets while preserving variance.
• Latent variable estimation through factor analysis and SEM for measurement models.
• Cluster discovery and pattern detection via various clustering techniques for exploratory analysis.
• Use Case: Analyze survey data to identify underlying constructs and group respondents by similarity.

Quick Start

Provide a dataset with labeled variables to run PCA, factor analysis, and clustering and obtain results.