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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