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Inter-laboratory proficiency testing
What is inter-laboratory proficiency testing? Proficiency testing, also called interlaboratory comparison, involves using statistical methods to compare the performance of several participants (which may be laboratories, inspection bodies, or individuals), referred to as “items” in XLSTAT, for specific measurements (referred to as “tests” in XLSTAT). Proficiency testing can be performed to assess...
Quantile regression
What is quantile regression Quantile regression keeps growing in importance and interest since it was introduced by Koenker and Basset in 1978. The method popularity among the practitioners and also researchers’ community is without doubt due to its peculiarity to provide them a realistic framework to perform their studies. Indeed, by nature, quantile regression enables to work with a wide range of...
Attribute charts
When to use an attribute control chart? Use this tool to supervise the production quality, in the case where you have a single measurement for each point in time. The measurements are based on attribute or attribute counts of the process. An attribute control chart is useful to recap the categorical variables of the measured production quality. Attribute control charts in XLSTAT Integrated in the...
Conjoint analysis
What is conjoint analysis? Conjoint analysis is a comprehensive method for the analysis of new products in a competitive environment. This tool allows you to carry out the step of analyzing the results obtained after the collection of responses from a sample of people. It is the fourth step of the analysis, once the attributes have been defined, the design has been generated and the individual responses...
Variable transformations
Use of variable transformation Variable transformation is often necessary to get a more representative variable for the purpose of the analysis. It can also be used simply to let your variable's distribution get closer to a normal distribution (notice that this does not work systematically). Deciding on the appropriate transformation will often improve the quality of your results. XLSTAT variable...
Regularized Generalized Canonical Correlation Analysis (RGCCA)
What is Regularized Generalized Canonical Correlation Analysis (RGCCA)? RGCCA is a method introduced by Tenenhaus et al. (2011). It allows to optimize a global function using an algorithm very similar to the PLSPM algorithm. Unlike the PLS approach, the results of the RGCCA are correlations between latent variables and between manifest variables and their associated latent variables (there is no regression...
Multicollinearity statistics
What is multicollinearity Variables are said to be multicollinear if there is a linear relationship between them. This is an extension of the simple case of collinearity between two variables. For example, for three variables X1, X2 and X3, we say that they are multicollinear if we can write: X1 = aX2 + bX3 where a and b are real numbers. How to detect multicollinearity To detect the multicolinearities...
k-means clustering
Description of the k-means clustering analysis in XLSTAT General description k-means clustering was introduced by McQueen in 1967. Other similar algorithms had been developed by Forgey (1965) (moving centers) and Friedman (1967). k-means clustering has the following advantages: An object may be assigned to a class during one iteration then change class in the following iteration, which is not possible...
DoE for choice based conjoint (CBC) analysis
Principle of design of experiments for choice based conjoint (CBC) analysis The principle of conjoint analysis is to present a set of products (also known as profiles) to the individuals who will note, class, or choose some of them. In an ideal analysis, individuals should test all possible products. But it is soon impossible; the capacity of each being limited and the number of combinations increases...
Dixon test for outliers
Principle of the test The Dixon test (1950, 1951, 1953), which is actually divided into six tests depending on the chosen statistic and on the number of outliers to identify, was developed to help determine if the greatest value or lowest value of a sample, or the two largest values, or the two smallest ones can be considered as outliers. This test assumes that the data correspond to a sample extracted...