The book categorizes methods based on the types of data you are analyzing:
: Principal Component Analysis (PCA) for quantitative 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 Practical Guide To Principal Component Methods ...
: Simple Correspondence Analysis (CA) for two variables and Multiple Correspondence Analysis (MCA) for more than two.
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
: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA) for datasets with both continuous and categorical variables.
: The book heavily utilizes the author's own factoextra R package , which creates elegant, ggplot2 -based graphs to help interpret results. The by Alboukadel Kassambara is widely considered an
: It simplifies complex statistical concepts into digestible pieces, focusing on intuitive explanations rather than advanced theory.