Practical Guide To Principal Component Methods ... < 2025 >

: Principal Component Analysis (PCA) for quantitative variables.

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

: 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 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.

: Hierarchical Clustering on Principal Components (HCPC), which combines dimensionality reduction with clustering techniques. Who Should Read It Core Methods Covered : Factor Analysis of Mixed

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

The book categorizes methods based on the types of data you are analyzing: Why It Is Highly Rated The book categorizes

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