Practical Guide To Principal Component Methods ... May 2026

: Those who need to analyze large multivariate datasets for research or business but prefer practical implementation over theoretical derivation.

: Principal Component Analysis (PCA) for quantitative variables.

: It simplifies complex statistical concepts into digestible pieces, focusing on intuitive explanations rather than advanced theory.

: It is structured with short, self-contained chapters and "R lab" sections that walk through real-world applications and tested code examples. Core Methods Covered

: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA) for datasets with both continuous and categorical variables.

: Simple Correspondence Analysis (CA) for two variables and Multiple Correspondence Analysis (MCA) for more than two.

: Hierarchical Clustering on Principal Components (HCPC), which combines dimensionality reduction with clustering techniques. Who Should Read It