Besides the mean comparisons between products or time-points, in consumer studies for example, usually Tops (1, 2, 3..) and Bottoms proportions/percentages are also required. As the number of questions is usually large, the inclusion of the result...

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.

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).

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) ...

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

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.

Would be nice to be able to integrate the area under the curve in linear and non-linear models.

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/

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...

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