XLSTAT version 2019.1
Discover our latest features and options
Data Selection: Run a Linear Regression or an ANOVA on millions of data points. Available under the Modeling data menu.
Test assumptions: Validate the hypothesis of normality and homogeneity of variances. Available under the Modeling data menu.
Influence diagnostics: Compute DFBetas, Mahalanobis distance and other influence statistics. Available under the Modeling data menu.
Variables characterization: Extra filtering and sorting options for a more customized output. It is also possible to use decimal weights in parametric tests. Available under the Describing data menu.
Nonlinear regression: New interface and additional built-in models which can be used in various fields such as pharmacology. Available under the Modeling data menu.
Scatterplots: Customize your graph choosing your own color for groups. Available under the Visualizing data menu.
Multiple Factor Analysis: You can now run this analysis on frequency tables such as count data of species. Available under the Sensory dataanalysis menu.
STATIS: This method can be particularly used in the case of projective mapping, conventional profiling, free choice profiling. Available under the Sensory data analysis menu.
DOE sensory: Improved designs for all sensory discrimination tests are now available. Available under the Sensory data analysis menu.
Customer Lifetime Value: A new and useful feature to assess the financial value of your customers. Available under the Marketing Tools menu.
Price Elasticity of Demand: Determine the price of your products at which the maximum revenue is generated. Available under the Marketing Tools menu.
How to get XLSTAT 2019.1?
If you are using the Trial version or you have a valid license, you can download the latest version at:
Theory and Practice with XLSTAT Marketing
This module focuses on Analysis of Variance, but this technique makes assumptions about the underlying distributions in our data
This course covers the excellent features in XLSTAT for investigating, visualising and modelling data sets with measurements on many variables.