Statistical Innovations logo






  CORExpress  go to section and expand
  Latent GOLD®  go to section and expand
  LG-Syntax Module  go to section and expand
  Latent GOLD® Choice  go to section and expand
  SI-CHAID®    go to section and expand
  GOLDMineR®  go to section and expand

    >
Tutorials
Free demo
Purchase






  XLSTAT-Pro
Products > XLSTAT-Pro
 


   PC System Requirements
      OS: 9x/Me/NT/2000/XP/Vista/Win 7
      Excel: 97 and later
      Drive Space: 45MB
      RAM: 128MB
      Processor: 800 MHz
      

Looking for an Alternative to SPSS® and SAS®?


XLSTAT is a complete statistics package as an Excel Add-in. Data is input into Excel and output is displayed in Excel--eliminating the risks associated with transferring your output into Excel. The user-friendly interface makes it easy to quickly analyze your data--no complicated syntax!

Several advanced add-on modules are available to increase the capabilities of XLSTAT-Pro, including XLSTAT-CCR and XLSTAT-LG from Statistical Innovations.



Features



The XSTAT-Pro base program includes the following capabilities:

  • Data Setup (categorical vs. continuous variables, recoding values, and more)
  • Describing Data (frequencies, cross-tabs, and more)
  • Visualizing Data (variety of plots)
  • Analyzing Data (factor analysis, principal components analysis, discriminant analysis, Kmeans cluster, and more)
  • Modeling Data (linear regression, ANOVA, logistic regression, and more)


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.




NEW: XLSTAT-LG

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.



Tutorials




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:


XLSTAT-CCR improves:



Tutorials




XLSTAT-Conjoint

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.

Features include:

  • 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



Tutorials



Special Offer!    Buy XLSTAT-Conjoint now and receive a 20% discount* on your future purchase or renewal of Latent GOLD Choice.




XLSTAT-Power

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.

Features include:

  • Compare means
  • Compare variances
  • Compare proportions
  • Compare correlations



Tutorials




XLSTAT-PLSPM

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.



Features include:

  • implements all methodological features and most recent findings of the PLEASURE (Partial LEAst Squares strUctural Relationship Estimation) technology.



Tutorials




XLSTAT-Life

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.



Features include:

  • Life table analysis
  • Kaplan-Meier analysis
  • Cox proportional hazard models
  • Sensitivity and specificity analysis
  • ROC Curves
  • Method comparison
  • Nelson-Aalen analysis
  • Cumulative incidence



Tutorials


Tutorials


All tutorials below are in .pdf format. You can download the Adobe PDF reader here.


Installation and Settings


      Running XLSTAT the first time (Excel 2007 and 2010)
      Running XLSTAT the first time (Excel 2004, Excel 2003 and earlier versions)
      Installing XLSTAT on Windows
      Updating Microsoft Excel
      Running XLSTAT with admin rights under Windows Vista or Windows 7
      Changing language settings
      Updating XLSTAT
      Closing XLSTAT
      Uninstalling XLSTAT


Getting Started


      Setting up an analysis in XLSTAT
      Selecting data with XLSTAT
      Creating and customizing a plot with XLSTAT
      Choosing the way the results are displayed
      Memorizing the data coordinates in the dialog box
      Save and reuse settings of an analysis, example of Principal Component Analysis
      Automate a routine analysis, example of Principal Component Analysis, in XLSTAT


XLSTAT-Pro


      Preparing Data
            Stratified data sampling with XLSTAT
            Coding and recoding data in XLSTAT
            Creating a contingency table in XLSTAT
            Discretizing a continuous variable
            Transforming the data with XLSTAT - Example of a Box-Cox transformation
            Automate a routine analysis, example of Principal Component Analysis, in XLSTAT
            Raking a survey sample using XLSTAT

      Describing Data
            Sampling data in a distribution and running a Normality test with XLSTAT
            Creating histograms and fitting a distribution in with XLSTAT
            Quantiles or percentiles with XLSTAT
            Generating Bootstrap statistics using Resampling in XLSTAT

      Visualizing Data
            Creating a scatter plot with XLSTAT
            Generating box plots with XLSTAT
            Creating a Parallel Coordinates Visualization with XLSTAT
            Chart with error bars with just two clicks
            Add a curve on a Microsoft Excel chart with XLSTAT
            Use XLSTAT functions within an Excel sheet
            Customizing a PCA chart with XLSTAT to make it easier to interpret

      Analyzing Data
            Running a Principal Component Analysis (PCA) with XLSTAT
            Running a Factor analysis with XLSTAT
            Running a Discriminant Analysis with XLSTAT
            Running a Correspondence Analysis (CA) from a contingency table with XLSTAT
            Running a Correspondence Analysis (CA) from a raw data table with XLSTAT
            Running a Multiple Correspondence Analysis (MCA) with XLSTAT
            Multidimensional Scaling (MDS) with XLSTAT
            Running an Agglomerative Hierarchical Clustering (AHC) with XLSTAT
            k-means clustering to group observations
            Clustering big datasets with XLSTAT - Using k-means clustering followed by an AHC

      Modeling data
            Fitting a distribution to a sample of data in XLSTAT?
            Running a one-way ANOVA followed by multiple comparisons tests with XLSTAT
            Running a two-way unbalanced ANOVA with interactions
            Running an ANCOVA in XLSTAT
            Running a simple linear regression in XLSTAT
            Multiple Linear Regression in XLSTAT
            Repeated measures ANOVA using the mixed models in XLSTAT
            Running a repeated measures ANOVA in XLSTAT
            Running a random components mixed model in XLSTAT
            Creating a CHAID classification tree with XLSTAT
            Nonparametric regression (kernel regression) with XLSTAT
            Running a multinomial logit model with XLSTAT
            Nonlinear regression with XLSTAT
            Non linear multiple regression with XLSTAT
            Logistic regression with XLSTAT

      Testing a Hypothesis
            Correlation Tests
                  Computing a Spearman correlation coefficient and test if it is significant or not
                  Cochran-Armitage trend test with XLSTAT
                  Running a Mantel test with XLSTAT

            Parametric Tests
                  Running a t-test or a z-test to compare the mean of a sample to a value in XLSTAT
                  Running a Fisher's F-test in XLSTAT to assess the equality of variance of 2 samples
                  Running a Student's t test on two independent samples
                  Comparing k > 2 variances using the Levene or Bartlett tests
                  Running a Friedman's test with XLSTAT
                  Test for comparing one proportion to a value in XLSTAT
                  Comparing two proportions in XLSTAT
                  Comparing k proportions with XLSTAT
                  Testing if two samples or more described by several variables are significantly different

            Nonparametric Tests
                  Running a Kruskal-Wallis test with XLSTAT
                  Running a Cochran-Mantel-Haenszel test with XLSTAT
                  Running a multinomial goodness of fit test with XLSTAT


XLSTAT-LG


      Tutorial 1: Latent Class cluster models
      Tutorial 2: Latent Class regression models




XLSTAT-CCR


      Tutorial 1: Getting Started with Correlated Component Regression (CCR) in XLSTAT-CCR
      Tutorial 2: Using CCR with a Dichotomous Y and Many Correlated Predictors
      Tutorial 3: Obtaining Predictions from a 2-class Regression


XLSTAT-Conjoint


      Conjoint analysis with XLSTAT-Conjoint
      Choice based conjoint analysis (CBC) with XLSTAT-Conjoint
      The conditional logit model with XLSTAT-Conjoint
      The monotone regression / MONANOVA method with XLSTAT-Conjoint


XLSTAT-Power


      Calculation of the required sample size or statistical power of a mean comparison test
      Calculating the required sample size or statistical power in a multiple regression


XLSTAT-PLSPM


      Create and run a basic PLS Path modeling project
      Comparing groups with XLSTAT-PLSPM
      Creating and running a basic XLSTAT-PLSPM project with Excel 2007
      Obtaining classes using the REBUS method with XLSTAT-PLSPM


XLSTAT-Life


      Generating a life table with XLSTAT-Life
      Running a Kaplan-Meier analysis with XLSTAT-Life
      Running a Nelson-Aalen analysis with XLSTAT-Life
      Running a Cumulative Incidence analyses with XLSTAT-Life
      Cox proportional hazards model with XLSTAT-Life
      Method comparison with the Bland Altman plot
      Sensitivity and specificity analysis with XLSTAT-Life
      Creating an ROC curve and identify the optimal threshold value for a detection method



You might also be interested in these programs by Statistical Innovations


CORExpress

CORExpress focuses on regression analysis (linear regression, logistic regression, etc.) where large numbers of correlated predictors may be available. On many data sets, it has been shown to outperform penalized regression techniques such as Lasso, and other methods such as Naive Bayes and PLS regression. CORExpress® (patent pending) develops reliable regression models using Correlated Component Regression (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).

After experiencing the power of CCR in XLSTAT-CCR, take your analysis to a new level with all of the features in CORExpress. Learn More

LG Choice

LG Choice 4.5 is a specialized program for conjoint and conditional logit models that takes ratings-based and choice-based conjoint modeling to another level by including latent class (LC) segments to allow for heterogeneity in the data. Typically, different segments have different preferences which show up in LC models as different utilities. After designing your conjoint or discrete choice study with XLSTAT-Conjoint, obtain new insights into these data using LG Choice 4.5. Learn More

Latent GOLD

Latent GOLD is #1 program for latent class (finite mixture) modeling.

Syntax Module

The Syntax module for Latent GOLD and LG Choice add many additional capabilities to these programs, including the ability to determine minimal sample sizes to achieve a certain power as well as many Monte Carlo and other power related calculations. Use XLSTAT-Power to perform power calculations for traditional models and then upgrade to Latent GOLD with LG-Syntax for extended power related calculations for the many kinds of latent class models that can be estimated by Latent GOLD or LG Choice.Learn More

"It seems that Vermunt and Magidson felt the need to add power to Latent GOLD and thought that flexibility could be better accessed through syntax rather than a more complicated GUI. This is almost like having a really sophisticated and powerful car, maybe a BMW M5, and then opening it up and adding even more power. One thing’s for sure—the power is there for the use, and it’s quite easy to program."

A Big Step Forward in Latent Class Analysis
by Ken Deal
Market Research Magazine, Fall 2010


Statistical Innovations is proud to announce its partnership with Addinsoft SARL, the developer of XLSTAT.

CORExpress®, Latent GOLD®, Latent GOLD® Choice, SI-CHAID®, & GOLDMineR® are trademarks of Statistical Innovations.
XLSTAT is a trademark of Addinsoft SARL.
Excel® is a trademark of the Microsoft Corporation.
SPSS® is a trademark of the IBM Corporation.
SAS® is a trademark of the SAS Institute Inc.




*Special expires 1 year after the date of purchase of XLSTAT-Conjoint, XLSTAT-Power, XLSTAT-PLSPM, and XLSTAT-Life modules. Special offer for purchasing CORExpress expires 2 years after the date of purchase of XLSTAT-CCR. Offer is non-transferrable and only available to customers who purchase associated XLSTAT modules directly from Statistical Innovations Inc. For full details, please contact Will Barker at support@statisticalinnovations.com.

E-mail Contact: support@statisticalinnovations.com
Address: Statistical Innovations, 375 Concord Avenue, Belmont, MA 02478-3084
Phone: +1.617.489.4490
Fax: +1.617.489.4499