Very useful in geosciences.

When performing the Kruskal-Wallis analysis of variance on k samples, up until now, you can choose between Dunn, Conover-Iman and Steel-Dwass-Critchlow-Fligner with the addition of Bonferroni correction (for the first two mentioned, respectively) ...

Hi, I have a question regarding the modeling data section. I have an XLSTAT-Premium version to deal with the metabolomics data. PCA and PLS-DA contributed a lot to explore my dataset. However, nowadays Orthogonal Partial least squares discriminant...

The quality of an ANCOVA depends on the heteroscedasticity and the normality of residuals. Currently, these two aspects are not part of the output of the ANCOVA analysis. They have to be manually calculated (in the basic version for normality, i...

In a linear hierarchy regression, it is important to know the change in R-squared to know if additional variables in a model increase the association with the predictor variables between the models (independent variables).

In the {Significance by cell (Fisher's exact test)} table, we can only get < or >.It would be valuable to have the corresponding p-values per cell.

Rolling multiple regression is extremely useful in that it enables you to see how stable the beta coefficients are over time. This is a very common issue that comes up regularly when you deal with financial time series.

It is possible to enter main effects and k-way interactions into a model now! Often it would be useful to also allow for quadratic and cubic terms .

Here is a series of references for checking for groups of outliers in the data: https://lemakistatheux.wordpress.com/category/tests-statistique-indices-de-liaison-et-coefficients-de-correlation/les-tests-pour-la-detection-doutliers/

I think XLSTAT provides partial least squares models but not general least squares. This would be very practical for time series analysis, for instance.