Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the pri
Revised and updated third edition offers a broader range of material Wide scope of methods and appli...
This book offers readers an accessible introduction to the world of multivariate statistics in the l...
This book offers a concise and well-organized introduction to multivariate statistical analysis meth...
Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which...
Review of: Exploratory multivariate analysis by example using R, by Francois Husson, Sebastien Le an...
With a useful index of notations at the beginning, this book explains and illustrates the theory and...
An exploratory functional tool for data analysis is presented In this paper, which is the principal...
Exploratory data analysis is one of the first steps in statistical data analysis. It includes the...
An introduction to data visualization that focuses on the explorative analysis of multivariate (tabu...
Hands-On Exploratory Data Analysis with R puts the complete process of exploratory data analysis int...
In a series of papers de Leeuw developed a general framework for multivariate analysis with optimal ...
Exploratory Factor Analysis (EFA) is frequently used in educational and social sciences. EFA has bee...
In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The mai...
International audienceIn this article, we present FactoMineR an R package dedicated to multivariate ...
R package : Shiny interfaces and graphical functions for multivariate analysis results exploration
Revised and updated third edition offers a broader range of material Wide scope of methods and appli...
This book offers readers an accessible introduction to the world of multivariate statistics in the l...
This book offers a concise and well-organized introduction to multivariate statistical analysis meth...
Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which...
Review of: Exploratory multivariate analysis by example using R, by Francois Husson, Sebastien Le an...
With a useful index of notations at the beginning, this book explains and illustrates the theory and...
An exploratory functional tool for data analysis is presented In this paper, which is the principal...
Exploratory data analysis is one of the first steps in statistical data analysis. It includes the...
An introduction to data visualization that focuses on the explorative analysis of multivariate (tabu...
Hands-On Exploratory Data Analysis with R puts the complete process of exploratory data analysis int...
In a series of papers de Leeuw developed a general framework for multivariate analysis with optimal ...
Exploratory Factor Analysis (EFA) is frequently used in educational and social sciences. EFA has bee...
In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The mai...
International audienceIn this article, we present FactoMineR an R package dedicated to multivariate ...
R package : Shiny interfaces and graphical functions for multivariate analysis results exploration
Revised and updated third edition offers a broader range of material Wide scope of methods and appli...
This book offers readers an accessible introduction to the world of multivariate statistics in the l...
This book offers a concise and well-organized introduction to multivariate statistical analysis meth...