This would be of general interest to a lot of people. It allows to understand how robust is your model if data is removed. See examples here: https://en.wikipedia.org/wiki/Sensitivity_analysis

The Tanimoto coefficient is the most widely used similarity metric in cheminformatics.

Confidence ellipses in pca illustrate the dissociation of groups and aid in the recognition of trends.

Hi, I am interested to explore regression tools in conjunction with Machine learning. Currently XLSTAT is not offering any regression solutions within Machine Learning. One of the areas to look into is SVM, where regression solutions are offered. ...

Given the cost of the software, it is dissapointing to see the XLSTAT-Forecast module does not include BASIC forecast evaluation statistics such as Diebold Mariano, Peseran-Timmerman, Correct Sign, RMSE, MSE, ME, MAE, MAPE etc - the first three st...

Dear scientists, I am doing PCA and Cluster analysis of water samples using XLSTAT 2018. To use PCA and cluster analysis, many researchers suggest that it is needed to normalization original data. In my case, I used the standardized method to tran...

Allows running a multivariate ANOVA while controlling for covariates. Especially useful in consumer research to understand consumer behaviour when exposing subjects to more than one independent variable (combinations of two treatments) but simulta...

Data is becoming ever more both high dimensional and sparse. Kernel PCA can enable a reliable and relatively simple dimensionality reduction. Kernels have already been introduced in Xlstat via SVMs.

It is useful in terms of crossvalidating the results and is implemented in other software