To predict the Overall Liking resulted from decreasing the percentages of Too Weak or Too Strong. This method would help clients in prioritizing sensory aspects to improve.

I'm a very happy and satisfied customer using XLSTAT. I've recently upgraded to MS Office 365 and was hoping that you could guide me to an App, or the process by which I can continue to use my XLStat with the online of MS Excel.

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

Introduction of kernel functions in PLS can significantly improve the explanatory power of regression. Hope it comes true in future.

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

Spatial Autocorrelation Indices (e.g Moran's I) are mainstream of spatial statistics that are worth being implemented in XLSTAT software. They are useful for ecological studies.

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).

important to know how many subjects are at risk at each major time interval. I have to look this up in the table and add it manually for each group being analyzed.

add some plot to show the panel's repeatability , consistency