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 from Statistical Innovations.

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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.

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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).

XLSTAST-CCR Improves the Following Features of Regression Models:

XLSTAT-CCR improves:

• interpretation of regression coefficients (see Tutorial 1)
• out-of-sample prediction (see Tutorial 2)
• classification (see Tutorial 2)
• latent class regression models (see Tutorial 3)
• variable selection (see Tutorials 1-3)

Tutorials

Special Offer!    Buy XLSTAT-CCR now and receive a 20% discount* on your future purchase or renewal of CORExpress.

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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.

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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

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.

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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

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

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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

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

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-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