XLStat full changelog

Friedman-Rafsky test:
The Friedman-Rafsky test has been added to our list of non-parametric tests. It allows to compare the distributions of two samples of quantitative data when they are described by more than one attribute/variable.
The interface gives you the choice between 4 different distances (Canberra, Chebychev, Euclidienne and Manhattan) and 3 algorithms to find the minimum spanning tree according to your data set (Chazelle using Soft-Heap, Kruskal and Boruvka).
An easy example: you know the weight and height of a Basketball team and a Football team. Use this test to determine if these two samples (teams) follow the same distribution. Click here to learn more.
This feature is available in all XLSTAT solutions (except Basic)
Similarity/Dissimilarity Matrices:
This feature allows you to calculate the similarities or dissimilarities between your observations or variables. XLSTAT offers many proximity measures to which we have added, in the context of binary data, two popular indices in supervised and unsupervised classification: the Rand and Adjusted Rand indices. These indices can be used to build a matrix before running a classification.
Moreover, it is now possible to enter large-volume data thanks to the file importation mode in XLSTAT.
ANOVA:
Several improvements have been added to our ANOVA functionality.
Improvement 1: Addition of groups
It is now possible to select a group variable in the General tab when your observations are associated with a group. This can be very helpful if your data is split into many sub-samples and you want to run an ANOVA for each subsample simultaneously.
Improvement 2: Add effect size measures
P-values tend to decrease when the sample size is increased. This may lead to falsely significant results in the case of large samples and thus to “wrong” interpretations.
For this reason, we have added effect size measures. They do not dependent on the sample size and allow better conclusions.
Improvement 3: Adding stars to the p-