Core statistical software
XLSTAT-Pro is Addinsoft's core software. It is a Microsoft Excel statistical add-in that has been developed since 1993 to enhance the analytical capabilities of Excel. XLSTAT-Pro includes a wide range of analytical functions covering the key requirements for data analysis and statistics.
3-D visualization software
XLSTAT-3DPlot is a must-have addition to our popular statistical analysis software. It facilitates the visualization of your data by using informative three dimensional charts. XLSTAT-3DPlot also offers impressive color and graphic possibilities that can make your presentations memorable.
Advanced Data Analysis on Multiple tables software
XLSTAT-ADA is a great software complement for users wanting to run Advanced Data Analysis. XLSTAT-ADA performs statistical data analysis on multiple tables such as canonical correspondence analysis, Generalized Procrustean Analysis or Principal coordinates analysis among others. These statistical methods are invaluable for a variety of sectors, ranging from ecology to marketing.
Conjoint analysis software
XLSTAT-Conjoint is a statistical software for marketers. It empowers you to find out the expectations of consumers towards new products and to model their choices thanks to conjoint analyses – crucial steps of a marketing analysis. Two methods of conjoint analysis are available: full profile conjoint analysis and choice based conjoint analysis (CBC).
XLSTAT-Conjoint analysis software is a complementary statistical program which allows you to run through all the analytical steps of conjoint analysis from the design of experiments to the simulation of new markets, through data analysis with specific regression methods – MONANOVA, multinomial logit, etc.
Correlated Component Regression software
The XLSTAT-CCR module focuses on regression analysis (linear regression, logistic regression, etc.) where a large number of correlated predictors may be available. On many data sets, Correlated Component Regression (CCR) has been shown to outperform penalized regression techniques such as Lasso, and other methods such as Naive Bayes and PLS regression.
XLSTAT-CCR develops reliable regression models using CCR methods. CCR models may even include more predictors than cases, a situation that is impossible with traditional regression methods. CCR was developed by Dr. Jay Magidson for simultaneously estimating regression models and excluding irrelevant predictors. Reliable models are obtained using a fast algorithm that incorporates M-fold cross-validation for tuning model parameters (optimal number of predictors and amount of regularization).
Design of Experiment Software
XLSTAT-DOE is a complement to XLSTAT-Pro for those who want to design experiments in a structured way. XLSTAT-DOE contains all the classic experimental designs for screening factors such as factorial designs or Plackett-Burman designs as well as designs for optimization.
Dose effect analysis software
XLSTAT-Dose is a statistical analysis MS Excel add-in complementary to XLSTAT-Pro that has been developed for dose analysis in the chemical and pharmaceutical industries. The software's main features are dose effect analysis including a large variety of model options (Logit, Probit, Gompertz, Log-log) and four-parameter logistic regression which enables fitting models of the type a-(d-a)/(1+(x/c)^b).
Survival analysis software
XLSTAT-Life is a statistical complement for survival analysis and life table analysis. This analytical software solution provides you with mainstream methods such as survival analysis using Kaplan-Meier analysis. It can also take into account competing risks with cumulative incidence, explanatory variables with Cox proportional hazards and parametric survival model, and the Nelson-Aalen method for estimating the hazard functions. ROC curves and sensitivity analysis are part of this module. Data analysis of different populations has never been easier!
Market research and sensory analysis software
XLSTAT-MX is a complementary statistical software dedicated for Market Research analysis. It is a must-have addition for XLSTAT-Pro users who deal with sensory data analysis, and use Preference Mapping, Penalty analysis or other related analytical techniques to provide invaluable insight into customers' behavior in order to identify directions for product improvement.
Pivot table software
XLSTAT-Pivot is a must-have software addition for XLSTAT-Pro to quickly create pivot tables. XLSTAT-Pivot module facilitates your data analysis and helps you to discover important trends and factors impacting your business by structuring your data in a way that allows you to see the most important information first; whereas the reporting tools available on the market today provide you with a lot of features to slice and dice your data they do not offer such synthetic analytical reports.
Partial Least Squares regression software
XLSTAT-PLS is a statistical Excel add-in with advanced modeling tools such as Partial Least Squares (PLS) regression and Principal Component regression (PCR). These regression methods free oneself from some of the constraints of the classical linear regression (OLS or WLS) and analysis of variance, such as the non-colinearity of the explanatory variables and the minimal sample size that must be greater than the number of explanatory variables. Thus, XLSTAT-PLS methods are excellent supplements to the tools contained in our core statistical analysis software - XLSTAT-Pro.
PLS Path Modelling software
XLSTAT-PLSPM - PLS Path Modeling Excel add-in - is the only complementary software that allows using the PLS Path Modeling approach without leaving Microsoft Excel. It is also the most complete offer on the market. The PLS path modeling approach is a powerful data exploration tool when concepts cannot be directly measured (the latent variables) and are interconnected - a causal graph can be drawn -, but relate to measured variables also called manifest variables. PLSPM is in many cases an alternative analysis to the SEM methods (Structural Equation Modeling), and a powerful analytical substitute in the cases where SEM cannot be used.
Statistical Power software
XLSTAT-Power is a compelling complementary software for computing and controlling the power of statistical tests or the minimal sample size of an experiment. Calculating the power or the type II error - also named beta risk - of a test beforehand is a key step in setting up an experiment, in order to test a hypothesis in the most efficient statistical way and a timesaver for your analysis. All XLSTAT-Power functions have been intensively tested against other software to guarantee fully reliable results, and to allow you to integrate this software in your Six Sigma business improvement process.
XLSTAT-Sim is a simulation software that allows you to create models with assessed risk in Microsoft Excel and uses simulation methods such as Monte Carlo and Latin Hypercubes simulations to estimate the distribution (including confidence intervals) of important variables. The supplementary XLSTAT-Sim module is a key decision making tool for people working on statistical risk analysis of models which may contain uncertain values. These uncertainties can be expressed through more than 30 distributions.
Statistical Process Control software
XLSTAT-SPC – a Microsoft Excel add-in – offers a powerful statistical analysis software solution for Statistical Process Control (SPC). XLSTAT-SPC is the ideal additional module for companies who apply Six Sigma methods to control and improve the quality of their production or sales processes. XLSTAT-SPC analytical control charts and Measurement System Analysis (MSA) tools, such as Gage Repeatability and Reproducibility for quantitative and qualitative data or Pareto plots, enable you to detect any process deviation.
Time series analysis software
XLSTAT-Time is a powerful statistical software for time series analysis and forecasting. XLSTAT-Time complements XLSTAT-Pro by providing you with outstanding functions to find out the degree of dependence between the values of a time series, to discover trends (seasonal or not), to apply specific pretreatments such as the Autoregressive Moving Average variants and finally to build predictive models.