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Internal preference mapping

What is preference mapping? Preference Mapping allows to build maps which show the preference of consumer for a type of product. A preference map is a decision support tool in analyses where a configuration of objects has been obtained from a first analysis (PCA, MCA, MDS), and where a table with complementary data describing the objects is available (attributes or preference data). There are two...

Factor analysis

What is Factor Analysis Exploratory factor analysis (or EFA) is a method that reveals the possible existence of underlying factors which give an overview of the information contained in a very large number of measured variables. The structure linking factors to variables is initially unknown and only the number of factors may be assumed. Latent factors used in Factor Analysis Three methods of extracting...

Multiple Factor Analysis (MFA)

Multiple Factor Analysis (MFA) makes it possible to analyze several tables of variables simultaneously, and to obtain results, in particular, charts, that allow studying the relationship between the observations, the variables, and tables (Escofier and Pagès, 1984). Within a table, the variables must be of the same type (quantitative table, qualitative table or frequency table), but the tables can...

Generalized Structured Component Analysis (GSCA)

What is Generalized Structured Component Analysis (GSCA) GSCA is a component based structural equation model method and can be used as PLS Path Modeling. This method introduced by Hwang and Takane (2011), allows to optimize a global function using an algorithm called Alternating Least Square algorithm (ALS). GSCA lies in the tradition of component analysis. It substitutes components for factors as...

Spectral analysis

Spectral analysis Spectral analysis is a powerful time series analysis method that lets you describe your data that is in the time domain, in the frequency domain. XLSTAT provides a complete Spectral analysis feature, which enables several options that will let you gain a deep insight on your data: Test if your time series (signal) is a white noise Estimate the spectral density by choosing one of...

Correspondence Analysis (CA)

What is Correspondence Analysis Correspondence Analysis is a powerful method that allows studying the association between two qualitative variables. It is based on the measure of the inertia. The aim of Correspondence Analysis is to represent as much of the inertia on the first principal axis as possible, a maximum of the residual inertia on the second principal axis and so on until all the total...

Running a Principal Component Analysis (PCA) with XLSTAT

Principal component analysis (PCA) in Excel

Canonical Correlation Analysis (CCorA)

Origins and aim of Canonical Correlation Analysis Canonical Correlation Analysis (CCorA, sometimes CCA, but we prefer to use CCA for Canonical Correspondence Analysis) is one of the many statistical methods that allow studying the relationship between two sets of variables.It studies the correlation between two sets of variables and extract from these tables a set of canonical variables that are as...

Time series descriptive statistics

One of the key issues in time series analysis is to determine whether the value we observe at time t depends on what has been observed in the past or not. If the answer is yes, then the next question is how. Autocovariances, autocorrelations, and partial autocorrelations The sample autocovariance function (ACVF) and the autocorrelation function (ACF) give an idea of the degree of dependence between...