The 'Inner Circle' option is now greyed out (only available for PLS).It is now automatically displayed in the Goodness of Fit table. Suppression of Press in the Outputs tab.This has been replaced by 'Regression diagnostics'' which contains Cooks and other indices. The Cooks checkbox has been removed from the Outputs tab of the dialog box.Several improvements and fixes have been made in this existing feature: A major advantage of PCR over classical regression is the ability to generate charts that clearly describe the data structure, such as the correlation chart and the biplot. Principal Component Regression (PCR) is built on Principal Components Analysis (PCA). Principal Component Regression (available in all XLSTAT solutions except Basic) It is also possible to import large-volume data files via the dedicated button in the dialog box (check our example ).Īccess this feature under the Analyzing Data menu. The results sheet has been restructured similarly to the Outputs tab. The first one offers generic outputs (descriptive statistics, eigenvalues, confusion matrix, ect.), while the second one offers various tests to validate specific assumptions of the model (Box test, Wilk's test, Pillai's trace, etc). The Outputs tab of the dialog box is now divided into two sub-tabs: General and Tests. Interactions between factors can be now taken into account to the model.ĭiscriminant Analysis (available in all XLSTAT solutions) A new table has been added to the report sheet allowing you to define the optimization settings of the analysis or use the default software settings. A revamped interface will allow you to easily generate a design for response surface analysis. Response surface designs are widely used to optimize various processes, like in the food industry, for example. Response Surface Designs (available in XLSTAT Life Sciences, Quality & Premium) The ROC curve is now displayed after the confusion matrix in the case of classification. A k-fold cross-validation is added for all proposed methods (regression, binary and multi-class classification). It allows you to solve quadratic problems faster thanks to second order information. Support Vector Machine (available in all XLSTAT solutions except Basic)Ī new algorithm is integrated for SVM classification (Fan 2005). Commonly used for fraud detection and machine fault diagnosis.Īccess this feature under the Machine Learning menu. The aim is to separate data into two classes (based on a decision function): the positive one, considered as the class of inliers, and the negative one, considered as the class of outliers. The One-class Support Vector Machine (One-class SVM) algorithm seeks to envelop underlying inliers.

One-class Support Vector Machine (available in all XLSTAT solutions except Basic) XLSTAT 2021.1 is now available! What’s new?

Xlstat pro rapidshare