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Factorial analysis of mixed data (PCAmix)
What is Factorial analysis of mixed data? Factorial analysis of mixed data is a method initially developed by Hill and Smith (1972). A few variants of this method have been developed since then (Escofier 1979, Pagès 2004). The method used in Xlstat is called PCAmix and was developed by Chavent et al (2014). This method can be seen as a mixture of two popular methods of factorial analysis: Principal...
Principal Coordinate Analysis
Principal Coordinate Analysis Principal Coordinate Analysis (PCoA) is a powerful and popular multivariate analysis method that lets you analyze a proximity matrix, whether it is a dissimilarity matrix, e.g. a euclidean distance matrix, or a similarity matrix, e.g. a correlation matrix. XLSTAT provides a PCoA feature with several standard options that will let you represent your data efficiently and...
Principal Component Analysis (PCA)
What is principal component analysis? Definition of a Principal Component Analysis Principal Component Analysis is one of the most frequently used multivariate data analysis methods that lets you investigate multidimensional datasets with quantitative variables. It is widely used in biostatistics, marketing, sociology, and many other fields. It is a projection method as it projects observations from...
Principal Component Regression (PCR)
What is Principal Component Regression PCR (Principal Components Regression) is a regression method that can be divided into three steps: The first step is to run a PCA (Principal Components Analysis) on the table of the explanatory variables, Then run an Ordinary Least Squares regression (OLS regression) also called linear regression on the selected components, Finally compute the parameters of the...
Partial Least Squares regression (PLS)
Partial Least Squares Regression (PLS) Partial Least Squares regression (PLS) is a quick, efficient and optimal regression method based on covariance. It is recommended in cases of regression where the number of explanatory variables is high, and where it is likely that there is multicollinearity among the variables, i.e. that the explanatory variables are correlated. XLSTAT provides a complete PLS...
Panel analysis
Use of Panel Analysis Use this tool to check whether your sensory or consumer panel allows to differentiate a series of products. If it does, measure to what extent and make sure that the ratings given by the assessors are reliable. Eight different types of analyses are performed so that you have a clear idea of how your panel performs whether globally or product by product. This unique feature saves...
Multiple Correspondence Analysis (MCA)
What is Multiple Correspondence Analysis Multiple Correspondence Analysis (MCA) is a method that allows studying the association between two or more qualitative variables. MCA is to qualitative variables what Principal Component Analysis is to quantitative variables. One can obtain maps where it is possible to visually observe the distances between the categories of the qualitative variables and between...
Redundancy analysis (RDA)
What is Redundancy Analysis Redundancy Analysis (RDA) was developed by Van den Wollenberg (1977) as an alternative to Canonical Correlation Analysis (CCorA). Redundancy Analysis allows studying the relationship between two tables of variables Y and X. While the Canonical Correlation Analysis is a symmetric method, Redundancy Analysis is non-symmetric. In Canonical Correlation Analysis, the components...
PLS Path Modelling
What is PLS Path Modeling? Partial Least Squares Path Modeling (PLS-PM) is a statistical approach for modeling complex multivariable relationships (structural equation models) among observed and latent variables. Since a few years, this approach has been enjoying increasing popularity in several sciences (Esposito Vinzi et al., 2007). Structural Equation Models include a number of statistical methodologies...
Canonical Correspondence Analysis (CCA and partial CCA)
What is Canonical Correspondence Analysis Canonical Correspondence Analysis (CCA) has been developed to allow ecologists to relate the abundance of species to environmental variables with the assumption that relationships are gaussian. However, this method can be used in other domains. Geomarketing and demographic analyses should be able to take advantage of it. Canonical Correspondence Analysis allows...