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).

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...

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.

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.

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...

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

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.

Wie kann mit Napping und UFP (ein oder mehrere Wiederholungen) Daten eine MFA/GPA in Excel aufgebaut werden?

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/