XLSTAT 2006

Addinsoft announces the availability of XLSTAT 2006, a true revolution in the small world of statistical add-ins for Excel. XLSTAT 2006 has been completely thought and written using new programming languages to make XLSTAT 2006 faster, even more reliable, easier to use, and open.

Here is a non-exhaustive list of what's new in XLSTAT 2006:

General software improvements

  • new dialog boxes: tabs allow to manage options for the computations and for the display of results.
  • two new languages, portuguese and japanese.
  • computations are now done in C++ allowing a significant increase in speed.
  • possibility in many functions to use transposed tables to circumvent the limitation of Excel to 256 columns.
  • possibility to use XLSTAT functions programmatically in VBA.
  • possibility to access the XLSTAT C++ functions programmatically from any environment.
  • more detailed help file.
  • only one software needs to be downloaded (the unique exe file includes all the modules).

XLSTAT-Pro

  1. Preparing data:
    • Distribution sampling: new distributions
  2. Describing data:
    • histograms: several histograms can be superimposed, and theoretical distributions can be plotted over the histograms.
    • new tool: multicolinearity statistics (Tolerances, VIFs).
    • new options in similarity/dissimilarity matrices.
  3. Visualizing data:
    • strip-plots.
    • more options for box plots.
    • new function to automatically modify a chart so that it is orthonormal (aspect ratio = 1).
    • new tool to merge two charts (for example, two curves available in 2 different charts).
    • more options for Easylabels.
    • new tool to modify the labels position.
  4. Analyzing data:
    • Factor analysis: polychoric correlations, better charts, new rotation options, maximum likelihood option.
    • PCA: polychoric correlations, better charts, new rotation options. New biplots.
    • CA/ MCA: new methods for subset analysis. Non symetrical correspondence analysis.
    • Discriminant analysis: better handling of multicolinearity cases, possiblity to include qualitative variables, stepwise options, cross-validation
    • Hierarchical clustering: dendrograms can now ne modified, groups can be shown with different colors.
    • K-Means : new clustering criteria.
  5. Modeling data:
    • Distribution fitting: new distributions.
    • Linear regression: better handling of multicolinearity cases, improved stepwise options.
    • ANOVA/ANCOVA: simpler interface for the multiple comparisons tests and better management of interactions.
    • Logistic regression: better handling of multicolinearity cases, PCR option, stepwise options, and multiple comparisons are possible when categorical variables have been selected.
  6. Association tests:
    • Correlation maps can now be displayed after correlation tests.
    • New association measures have been added.
    • Mantel test has been improved.
  7. Parametric tests:
    • possibility to run tests on several variables in one run.
    • easier management of alternative hypotheses.
  8. Nonparametric tests:
    • exact tests are now possible for most methods.
  9. Tools:
    • easier to use DataFlagger.
    • tool to remove hidden sheets with possibility to select only some of them.
    • tool to find the min and/or the max in a sheet.

XLSTAT-3DPlot

  • New interface with many new options

XLSTAT-MX

  • PREFMAP: simplified interface, possibility to add contour plots
  • GPA: permutation tests, new options to display results

XLSTAT-Time

  • Possibility to use dates to display the results, and confidence intervals in ARIMA can be used on long time series.

XLSTAT-Life

  • Comparison tests after Kaplan-Meier analyses and life table analyses can now be applied to more than two groups.

XLSTAT-Pivot

  • simplified interface, quicker computations.

Latest events

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.

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