The inconsistency vector in AHP gives information about the inconsistency which must be linked to the multidimensionality of the method.

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...

Estimating lagged prediction models from autocorrelated historical data is a common challenge in business and economics. The resulting multicollinearity causes the models to perform poorly. The Almon lag method is a clever way to deal with the mul...

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.

The Nash-Sutcliffe efficiency , Willmott's index and Model Performance Index (MPI) are suitable indices to add XLSTAT for assessing model performance.

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...

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/

The scatterplot matrix is a great way to quickly visualize correlations among multivariate data. It is especially helpful if confidence ellipses can be added and if the axes can be easily edited.

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.

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