Practical Guide To Principal Component Methods ... -

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

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

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

: 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

The by Alboukadel Kassambara is widely considered an excellent resource for those who want to apply multivariate analysis without getting bogged down in heavy mathematical proofs. Why It Is Highly Rated : Simple Correspondence Analysis (CA) for two variables

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

: Those who need to analyze large multivariate datasets for research or business but prefer practical implementation over theoretical derivation. Who Should Read It : It is structured

: Principal Component Analysis (PCA) for quantitative variables.