Featured XLSTAT Add-on Modules
The following modules require the current version of XLSTAT-Pro. These and additional modules not listed below are available for purchase in the online store.
XLSTAT-CCR: Correlated Component Regression
XLSTAT-CCR develops reliable regression models using Correlated Component Regression (CCR) methods. CCR models may be developed even when you have more predictors than cases, a situation where it is impossible to obtain reliable predictive models 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 to determine optimal values for the 2 tuning parameters (P* = optimal number of predictors, and K* = optimal amount of regularization). Click here to view full details on XLSTAT-CCR.
Regression modeling is undergoing a revolution precipitated by the availability of hundreds and even thousands of candidate predictor variables in genomics, but increasingly vast amounts of data are becoming available in all other fields as well. Problems in traditional regression modeling occur when the number of predictors P included in a model approaches or exceeds the sample size N. In this situation, which involves ‘high-dimensional data’, traditional regression methods become unreliable and regression coefficients may even be impossible to estimate. Recent advances in high-dimensional data analysis show how such problems can be resolved (see: Cai and Shen (2011)). This important new field continues to evolve at a rapid pace.
- XLSTAT-CCR develops improved regression and classification models for:
- linear regression
- logistic regression
- linear discriminant analysis
- XLSTAT-CCR handles multicolinearity due to correlated predictors effectively even with high dimensional data (more variables than cases).
XLSTAT-CCR Improves the Following Features of Regression Models:
Generate designs and analyze data obtained from ratings-based conjoint and discrete choice experiments.
XLSTAT-Conjoint is a statistical software package for marketing researchers. It helps reveal consumer expectations towards new products and to model their choices based on relevant product attributes -– crucial steps in conjoint analysis. Two methods of conjoint analysis are supported: full profile conjoint analysis and choice based conjoint analysis (CBC).
XLSTAT-Conjoint analysis software is a complete package which allows you to perform all the analytical steps of conjoint analysis from generating the experimental design to the the development of new market simulations based on specific regression methods – MONANOVA, multinomial logit, etc.
- Experiment Designs for ratings-based conjoint analysis
- Experiment Designs for choice-based conjoint analysis
- Ratings-based conjoint analysis
- Choice-based conjoint analysis
- Market simulations for conjoint analysis
- MONANOVA - Monotone regression
- Conditional logit model
Special Offer! Buy XLSTAT-Conjoint now and receive a 20% discount* on your future purchase or renewal of Latent GOLD Choice.
For power analysis calculations, calculating sample size for a planned study, plus much more.
XLSTAT-Power is a powerful software solution for computing and controlling the power of statistical tests or determining the minimal sample size required to achieve adequate power. Calculating the power or type II error - also named beta risk - of a test beforehand is a key step in setting up an experiment to test a hypothesis in the most efficient statistical manner, and a timesaver for your analysis.
All XLSTAT-Power functions have been intensively tested against other software to guarantee the users fully reliable results, and to allow you to integrate this software in your Six Sigma business improvement process.
- Compare means
- Compare variances
- Compare proportions
- Compare correlations
Special Offer! Buy XLSTAT-Power now and receive a 20% discount* on your future purchase or renewal of the LG-Syntax Module. The LG-Syntax module extends power calculations to latent class models and much more.
A powerful PLS Path Modeling approach, XLSTAT-PLSPM allows you to build the graphical representation of the model, then to fit the model, and display the results in Excel either as tables or graphical views.
XLSTAT-PLSPM -- PLS Path Modeling Excel add-in -- is the only software that allows using the PLS Path Modeling approach without leaving Microsoft Excel. This approach is a powerful data exploration tool when concepts cannot be directly measured (the latent variables) but may be interconnected - a causal graph can be drawn, and they relate to measured (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.
- implements all methodological features and most recent findings of the PLEASURE (Partial LEAst Squares strUctural Relationship Estimation) technology.
Special Offer! Buy XLSTAT-PLSPM now and receive a 20% discount* on your future purchase or upgrade of Latent GOLD.
XLSTAT-Life is an important statistical package for survival analysis and life table analysis. This analytical software solution provides you with leading-edge methods such as survival analysis using Kaplan-Meier analysis and Cox proportional hazards model. Moreover, advanced capabilities allow you to take competing risks into account with cumulative incidence, and use the Nelson-Aalen linear method for estimating the hazard functions.
- Life table analysis
- Kaplan-Meier analysis
- Cox proportional hazard models
- Sensitivity and specificity analysis
- ROC Curves
- Method comparison
- Nelson-Aalen analysis
- Cumulative incidence
Special Offer! Buy XLSTAT-Life now and receive a 20% discount* on your future purchase or upgrade of Latent GOLD.