The XLSTAT-Premium solution, all the data analysis tools you need in ExcelWhat is XLSTATXLSTAT is a leader in software for statistical analysis in Excel
Since 1993, we have worked continuously to bring you and some other 50,000 customers from more than 100 countries a powerful, versatile, and above all user-friendly and affordable software to meet all of your statistical needs.
Featuring over 200 standard and advanced statistical tools, XLSTAT works as a seamless add-on to MS Excel®, allowing you to easily analyze and reformat your data within Excel. XLSTAT is compatible with both PC and Mac.
XLSTAT uses pioneering computing techniques so that you get actionable results at unbeatable speeds: parallel computing allows you to take full advantage of all your computer processors.
Today XLSTAT offers a wide variety of industry/field specific solutions designed for each and every one of you.
So make way for a statistical software that will change the way you work.
The XLSTAT-Premium solutionXLSTAT-Premium is an advanced yet affordably priced statistical solution that includes all of the 200+ XLSTAT features currently available. In XLSTAT-Premium we have included it all so you’ll have access to any method, any time.
Prepare data, visualize, explore, analyze, take decisions, predict. Take advantage of all of that data analysis tools offer today in one powerful yet user-friendly software that will reveal everything your data has to say in just a few clicks.

Sensory data analysis
Preference Mapping (PREFMAP)
Generalized Procrustes Analysis (GPA)
Multiple Factor Analysis (MFA)
Penalty analysis
Product characterization
DOE for sensory data analysis
TURF analysis
Internal preference mapping
Panel analysis
Generalized Bradley-Terry model
Sensory shelf life analysis
CATA data analysis
Sensory discrimination tests
Design of experiments for sensory discrimination tests
Conjoint analysis
DoE for conjoint analysis
DoE for choice based conjoint (CBC) analysis
Conjoint analysis
Choice based conjoint analysis
Simulation for conjoint analysis
MONANOVA - Monotone regression
Conditional Logit model
MaxDiff analysis
Time series analysis
Fourier transform
Mann-Kendall Trend Tests
Homogeneity tests for time series
Spectral analysis
Time series descriptive statistics
Time series transformation
Smoothing of time series
ARIMA
Cochrane-Orcutt model
Durbin-Watson test
Unit root and stationarity tests
Heteroscedasticity tests
Cointegration tests
Monte Carlo simulations
Monte Carlo simulations
Power analysis
Statistical Power for mean comparison
Statistical Power to compare variances
Statistical Power for proportion comparison
Statistical Power for comparing correlations
Statistical Power for linear regression
Statistical Power for ANOVA / ANCOVA / Repeated measures ANOVA
Statistical Power for logistic regression
Statistical Power for Cox model
Sample size for clinical trials
Statistical Process Control
Subgroup charts
Individual charts
Attribute charts
Time Weighted charts
Pareto charts
Gage Repeatability and Reproducibility (Quantitative)
Gage Repeatability and Reproducibility for Attributes
Design of Experiments
Screening designs
Surface response designs
Analysis of a screening design
Analysis of a surface response design
Mixture design
Analysis of a mixture design
Survival analysis
Sensitivity and specificity analysis
ROC curves
Nelson-Aalen analysis
Cumulative incidence
Life table analysis
Kaplan-Meier analysis
Cox proportional hazards models
Parametric survival regression (Weibull model)
Parametric survival curves
Method validation
Method comparison (Bland Altman, …)
Passing and Bablok regression
Deming regression
OMICS data analysis
Differential expression
Heat map
Multiblock data analysis
Generalized Procrustes Analysis (GPA)
Canonical Correspondence Analysis (CCA and partial CCA)
Multiple Factor Analysis (MFA)
Redundancy analysis (RDA)
Canonical Correlation Analysis (CCorA)
Path modeling
PLS Path Modelling
Regularized Generalized Canonical Correlation Analysis (RGCCA)
Generalized Structured Component Analysis (GSCA)
Dose effect analysis
Dose effect analysis
Four/Five-parameter parallel lines logistic regression
XLSTAT-Base
Preparing data
k-means clustering
Classification and regression trees
Gaussian mixture models
Association rules
K Nearest Neighbors (KNN)
Naive Bayes classifier
Describing data
Descriptive statistics (including Box plots and scattergrams)
Histograms
Normality tests
Contingency table (descriptive statistics)
Similarity/Dissimilarity matrices (correlation…)
Multicollinearity statistics
Quantiles estimation
Resampled statistics
Pivot table
Biserial correlation
Variable characterization
Analyzing data
Factor analysis
Principal Component Analysis (PCA)
Discriminant Analysis (DA)
Correspondence Analysis (CA)
Multiple Correspondence Analysis (MCA)
Multidimensional Scaling (MDS)
Agglomerative Hierarchical Clustering (AHC)
k-means clustering
Univariate clustering
Principal Coordinate Analysis
Gaussian mixture models
Visualizing data
Histograms
Scatter plots
Parallel coordinates plots
Semantic differential charts
Error bars
Plot a function
Univariate plots
Plot management
Ternary diagrams
Modeling data
Distribution fitting
Linear regression
ANOVA (Analysis of variance)
ANCOVA (Analysis of Covariance)
Logistic regression (Binary, Ordinal, Multinomial, …)
Nonlinear regression
Nonparametric regression (Kernel and Lowess)
Mixed models
Repeated measures Analysis of Variance (ANOVA)
Ordinary Least Squares regression (OLS)
Principal Component Regression (PCR)
Partial Least Squares regression (PLS)
PLS discriminant analysis
Welch and Brown-Forsythe one-way ANOVA
Ordinal logit model
Log-linear regression (Poisson regression)
Two-stage least squares regression
Cubic splines
Quantile regression
Multivariate Analysis of Variance (MANOVA)
Correlation/Association tests
Tests on contingency tables
Correlation tests
Mantel test
Cochran-Armitage trend test
Biserial correlation
RV coefficient
Parametric tests
One-sample t-test and z-test
Two-sample t-test and z-test
Two-sample comparison of variances
k-sample comparison of variances
Test for one proportion
Test for two proportions
k proportions test
Multidimensional tests (Mahalanobis, …)
Multinomial goodness of fit test
TOST (Equivalence test)
One-sample variance test
Nonparametric tests
Non parametric tests on two independent samples
Non parametric tests on two paired samples
Kruskal-Wallis test
Friedman test
Cochran's Q test
McNemar's test
One sample runs test
Cochran-Mantel-Haenszel test
Durbin and Skillings-Mack tests
Page test
Mood test (Median test)
Tools
Export to GIF/JPG/PNG/TIFF
Manage data (DataFlagger, MinMaxSearch, Remove text values in a selection)
Manage workbook (Sheets management, Delete hidden sheets, Show hidden sheets)
Manage the menu bars (Display the main bar, Hide the sub-bars)
Testing for outliers
Grubbs' test for outliers
Dixon test for outliers
Cochran C test for outlying variances
Mandel’s h and k statistics for outliers
Machine Learning
k-means clustering
Classification and regression trees
Gaussian mixture models
Association rules
K Nearest Neighbors (KNN)
Naive Bayes classifier

Windows Versions
XP / Vista / Win 7 / Win 8 / Win 10
Excel
2003, 2007, 2010, 2013, 2016
Processor
32 or 64 bits
Hard disk
150 Mb

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