Practical Guide To Principal Component Methods ... May 2026

: Principal Component Analysis (PCA) for quantitative variables.

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

: The book heavily utilizes the author's own factoextra R package , which creates elegant, ggplot2 -based graphs to help interpret results. Practical Guide To Principal Component Methods ...

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

: 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

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 and Multiple Correspondence Analysis (MCA) for more than two. Why It Is Highly Rated : Simple Correspondence

The book categorizes methods based on the types of data you are analyzing: