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-LG is a powerful tool that uses Latent Classes. It is based on two modules from Latent GOLD® 5.0: Latent Class Cluster models and Latent Class Regression models . Both model families offer unique features compared to traditional clustering or regression approaches. XLSTAT-LG offers a wide variety of easily implementable options that allow the user to gain full control over the Latent Class models.
Latent class analysis involves the construction of Latent Classes which are unobserved (latent) subgroups or segments of cases. The latent classes are constructed based on the observed (manifest) responses of the cases on a set of indicator variables. Cases within the same latent class are homogeneous with respect to their responses on these indicators, while cases in different latent classes differ in their response patterns.
Formally, latent classes are represented by K distinct categories of a nominal latent variable X.. Since the latent variable is categorical, Latent Class modeling differs from more traditional latent variable approaches such as factor analysis, structural equation models, and random-effects regression models since these approaches are based on continuous latent variables.
XLSTAT-LG is based on the Latent Gold® software developed by Statistical Innovations inc.
Advantages of Latent Class cluster models over more traditional clustering methods:
Advantages of Latent Class cluster models over more traditional ad-hoc types of cluster analysis methods include model selection criteria and probability-based classification. Posterior membership probabilities are estimated directly from the model parameters and used to assign cases to the modal class - the class for which the posterior probability is highest.
Furthermore, it is possible to include variables of different scales (continuous, ordinal or nominal) within the same model. These variables are called indicators.
A special feature of Latent Class cluster models is the ability to obtain an equation for calculating these posterior membership probabilities directly from the observed variables (indicators). This equation is called the scoring equation. It can be used to score new cases based on a LC cluster model estimated previously. That is, the equation can be used to classify new cases into their most likely latent class as a function of the observed variables. This feature is unique to LC models – it is not available with any other clustering technique.
XLSTAT-LG allows launching computations automatically on different models according to different number of classes. It is also possible to optimize Bayes constants, sets of random starting values, as well iteration parameters for both the Expectation-Maximization and Newton-Raphson algorithms, which are used for model estimation.
A Latent Class cluster model:
- Includes a nominal latent variable X with K categories, each category representing a cluster.
- Each cluster contains a homogeneous group of persons (cases) who share common interests, values, characteristics, and/or behavior (i.e., share common model parameters).
- These interest, values, characteristics, and/or behavior constitute the observed variables (indicators) Y upon which the latent clusters are derived.
A Latent Class regression model:
- Is used to predict a dependent variable as a function of predictor variables (Regression model).
- Includes a K-category latent variable X to cluster cases (LC model).
- Each category represents a homogeneous subpopulation (segment) having identical regression coefficients (LC Regression Model).
- Each case may contain multiple records (Regression with repeated measurements).
- The appropriate model is estimated according to the scale type of the dependent variable:
- Continuous: Linear regression model (with normally distributed residuals).
- Nominal (with more than 2 levels): Multinomial logistic regression.
- Ordinal (with more than 2 ordered levels): Adjacent-category ordinal logistic regression model.
- Count: Log-linear Poisson regression.
- Binomial Count: Binomial logistic regression model.
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
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.
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