XLSTAT Features overview

Preparing data
Data sampling
Distribution Sampling
Discretization
Coding
Coding by ranks
Presence/Absence coding
Missing data
Complete disjunctive tables (Creating dummy variables)
Create contingency tables
Variable transformations
Data management
Raking survey data

Describing data
Descriptive statistics (including Box plots and scattergrams)
Histograms
Reliability Analysis
Normality tests
Contingency table (descriptive statistics)
Similarity/Dissimilarity matrices (correlation…)
Multicollinearity statistics
Quantiles estimation
Resampled statistics
Biserial correlation
Variable characterization
Pivot table

Analyzing data
Principal Component Analysis (PCA)
Factorial analysis of mixed data (PCAmix)
Correspondence Analysis (CA)
Multiple Correspondence Analysis (MCA)
Principal Coordinate Analysis
Multidimensional Scaling (MDS)
Factor analysis
Discriminant Analysis (DA)
Agglomerative Hierarchical Clustering (AHC)
k-means clustering
Univariate clustering
Gaussian mixture models

Modeling data
Distribution fitting
Linear regression
ANOVA (Analysis of variance)
Welch and Brown-Forsythe one-way ANOVA
ANCOVA (Analysis of Covariance)
Multivariate Analysis of Variance (MANOVA)
Logistic regression (Binary, Ordinal, Multinomial, …)
Ordinal logit model
Log-linear regression (Poisson regression)
Quantile regression
Cubic splines
Nonparametric regression (Kernel and Lowess)
Nonlinear regression
Partial Least Squares regression (PLS)
PLS discriminant analysis
Repeated measures Analysis of Variance (ANOVA)
Mixed models
Ordinary Least Squares regression (OLS)
Principal Component Regression (PCR)
Two-stage least squares regression

Machine Learning
Classification and regression trees
Association rules
K Nearest Neighbors (KNN)
Classification and regression random forests
Naive Bayes classifier
Support Vector Machine
k-means clustering
Gaussian mixture models
Fuzzy k-means clustering

Sensory data analysis
Preference Mapping (PREFMAP)
Internal preference mapping
Penalty analysis
Product characterization
Panel analysis
CATA data analysis
Temporal Dominance of Sensations (TDS)
Time-Intensity
TURF analysis
Generalized Bradley-Terry model
Sensory shelf life analysis
Design of experiments for sensory discrimination tests
Sensory discrimination tests
DOE for sensory data analysis
Multiple Factor Analysis (MFA)
Generalized Procrustes Analysis (GPA)
Sensory wheel
STATIS
CATATIS
TCATA

Conjoint analysis
DoE for conjoint analysis
Conjoint analysis
DoE for choice based conjoint (CBC) analysis
Choice based conjoint analysis
MaxDiff analysis
Simulation for conjoint analysis
MONANOVA - Monotone regression
Conditional Logit model

Time series analysis
Time series descriptive statistics
Time series transformation
Smoothing of time series
ARIMA
Mann-Kendall Trend Tests
Cointegration tests
Unit root and stationarity tests
Homogeneity tests for time series
Heteroscedasticity tests
Durbin-Watson test
Cochrane-Orcutt model
Fourier transform
Spectral analysis

Monte Carlo simulations (only for Windows OS)
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
Individual charts
Subgroup charts
Attribute charts
Time Weighted charts
Pareto charts
Gage Repeatability and Reproducibility (Quantitative)
Gage Repeatability and Reproducibility for Attributes

Design of Experiments
Screening designs
Analysis of a screening design
Surface response designs
Analysis of a surface response design
Mixture design
Analysis of a mixture design

Survival analysis
Life table analysis
Kaplan-Meier analysis
Cox proportional hazards models
Proportional Hazards Model with interval censored data
Sensitivity and specificity analysis
ROC curves
Nelson-Aalen analysis
Cumulative incidence
Parametric survival regression (Weibull model)
Parametric survival curves
Propensity Score Matching

Method validation
Method comparison (Bland Altman, …)
Passing and Bablok regression
Deming regression

OMICS data analysis
Differential expression
Heat map

Multiblock data analysis
Canonical Correspondence Analysis (CCA and partial CCA)
Canonical Correlation Analysis (CCorA)
Redundancy analysis (RDA)
Generalized Procrustes Analysis (GPA)
Multiple Factor Analysis (MFA)
STATIS

Path modeling (only for Windows OS)
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

3DPlot		(optional)	(optional)	(optional)	(optional)	(optional)	(optional)	(optional)
3D visualization		(optional)	(optional)	(optional)	(optional)	(optional)	(optional)	(optional)

LatentClass		(optional)	(optional)	(optional)	(optional)	(optional)	(optional)	(optional)
Latent Class cluster models		(optional)	(optional)	(optional)	(optional)	(optional)	(optional)	(optional)
Latent Class regression models		(optional)	(optional)	(optional)	(optional)	(optional)	(optional)	(optional)

XLSTAT-R
Self-Organizing Maps (SOM)
Permutational MANOVA or adonis
Panel regression
Rank-based regression
Neural Networks
Partitioning Around Medoids
Dissimilarity matrix for mixed data
GARCH
Kriging

Decision aid
ELECTRE methods
Design of experiments for the analytic hierarchy process (DHP)
Analytic Hierarchy Process (AHP)