The STATISTICA desktop line of products is designed for deployment on a single workstation. STATISTICA spreadsheets, configurations, and macros are all stored on the user's local workstation as a standalone application. STATISTICA's desktop products are licensed very flexibly, allowing you to select from the list of STATISTICA's analytic capabilities to suit your needs.
STATISTICA includes not only general purpose statistical, graphical, and analytic data management procedures, but also comprehensive implementations of specialized methods for data analysis (e.g., data mining, business, social sciences, biomedical research, or engineering applications). All analytic tools offered in the STATISTICA line of software are available to you as part of an integrated package.
Explanation of Products
STATISTICA Base
Offers a comprehensive set of essential statistics in a userfriendly package and all the performance, power, and ease of use of the STATISTICA technology. STATISTICA Base is compatible with Windows 2000, Windows XP, and Windows Vista. It features all the graphics tools in STATISTICA and the following modules:

Descriptive Statistics, Breakdowns, and Exploratory Data Analysis

Correlations

Basic Statistics from Results Spreadsheets (Tables)

Interactive Probability Calculator

TTests (and other tests of group differences)

Frequency Tables, Crosstabulation Tables, StubandBanner Tables, Multiple Response Analysis

Multiple Regression Methods

Nonparametric Statistics

ANOVA/MANOVA

Distribution Fitting
STATISTICA Advanced
Includes the functionality of all of the following:

STATISTICA Base

STATISTICA Multivariate Exploratory Techniques offers a broad selection of exploratory techniques, from cluster analysis to advanced classification trees methods, with a comprehensive array of interactive visualization tools for exploring relationships and patterns; builtin complete Visual Basic scripting.

Cluster Analysis Techniques

Factor Analysis and Principle Components

Canonical Correlation Analysis

Reliability/Item Analysis

Classification Trees

Correspondence Analysis

Multidimensional Scaling

Discriminant Analysis

General Discriminant Analysis Models

STATISTICA Visual Basic Language, and more.

STATISTICA Advanced Linear/Nonlinear Models contains a wide array of the most advanced linear and nonlinear modeling tools on the market, supports continuous and categorical predictors, interactions, hierarchical models; automatic model selection facilities; also, includes variance components, time series, and many other methods; all analyses include extensive, interactive graphical support and builtin complete Visual Basic scripting.

Distribution and Simulation

Variance Components and Mixed Model ANOVA/ANCOVA

Survival/Failure Time Analysis

Cox Proportional Hazard Models

General Nonlinear Estimation (and Logit/Probit)

LogLinear Analysis

Time Series Analysis, Forecasting

Structural Equation Modeling/Path Analysis (SEPATH)

General Linear Models (GLM)

General Regression Models (GRM)

Generalized Linear/Nonlinear Models (GLZ)

Partial Least Squares (PLS)

STATISTICA Visual Basic Language, and more.

STATISTICA Power Analysis and Interval Estimation is an extremely precise and userfriendly research tool for analyzing all aspects of statistical power and sample size calculation.

Power Calculations

Sample Size Calculations

Interval Estimation

Probability Distribution Calculators, and more.
Features
Descriptive Statistics, Breakdowns and Exploratory Data Analysis
Descriptive Statistics and Graphs
STATISTICA Base will compute practically all common, generalpurpose descriptive statistics including:

medians

modes

quartiles

userspecified percentiles

average and standard deviations

quartile ranges

confidence limits for the mean

skewness and kurtosis (with their respective standard errors)

harmonic means

geometric means
Other specialised descriptive statistics and diagnostics, either for all cases or broken down by one or more categorical (grouping) variables are also calculated. A wide variety of graphs will aid exploratory analyses:

various types of boxandwhisker plots

histograms

bivariate distribution (3D or categorised) histograms

2D and 3D scatterplots with marked subsets

normal, halfnormal, detrended probability plots

QQ plots,

PP plots
A selection of tests is available for fitting the normal distribution to the data via the KolmogorovSmirnov, Lilliefors, and ShapiroWilks' tests. Facilities for fitting a wide variety of other distributions are also available.
ByGroup Analyses (Breakdowns)
Practically all descriptive statistics as well as summary graphs can be computed for data that are categorised by one or more grouping variables. For example, with just a few mouse clicks the user can break down the data by Gender and Age and review categorised histograms, boxandwhisker plots, normal probability plots, scatterplots. If more than two categorical variables are chosen, cascades of the respective graphs can be automatically produced. Options to categorise by continuous variables are provided. For example, you can specify that a variable be split into a requested number of intervals, or use the online recode facility to customdefine the way in which the variable will be recoded. Categorisation options of practically unlimited complexity can be specified at any point and they can reference relations involving all variables in the dataset.
In addition, a specialised hierarchical breakdown procedure is provided that allows the user to categorise the data by up to six categorical variables and compute a variety of categorised graphs, descriptive statistics, and correlation matrices for subgroups. The user can interactively request to ignore some factors in the complete breakdown table, and examine statistics for any marginal tables. Numerous formatting and labelling options allow the user to produce publicationquality tables and reports with long labels and descriptions of variables. Note that extremely large analysis designs can be specified in the breakdown procedure and results include ANOVA statistics. This includes the complete ANOVA table, tests of assumptions such as the Levene and BrownForsythe tests for homogeneity of variance, a selection of seven posthoc tests and more.
Extended precision calculations (the "quadruple" precision, where applicable) are used to provide an unmatched level of accuracy. Because of the interactive nature of the program, exploration of data is very easy. For example, exploratory graphs can be produced by pointing with the mouse to specific cells or ranges of cells. Cascades of complex (e.g., multiple categorised) graphs can be produced with a singleclick of the mouse and reviewed in a slideshow manner. In addition to numerous predefined statistical graphs, countless graphical visualisations of raw data, summary statistics, relations between statistics, as well as all breakdowns and categorisations can be customdefined by the user via straightforward pointandclick facilities. All exploratory graphical techniques are integrated with statistics to facilitate graphical data analyses.
Correlations
A comprehensive set of options allows for the exploration of correlations and partial correlations between variables. Practically all common measures of association can be computed:

Pearson r

Spearman rank order R

Kendall tau (b, c), Gamma

tetrachoric r

Phi

Cramer V

contingency coefficient C

Sommer's D

uncertainty coefficients

part and partial correlations

autocorrelations

various distance measures
Correlation matrices can be computed using casewise (listwise), pairwise deletion of missing data, or mean substitution. Precision calculations (the quadruple precision, where applicable) are used to yield an unmatched level of accuracy. Correlation matrices are displayed in Spreadsheets offering various formatting options and extensive facilities to visualise numerical results. The user can point to a particular correlation in the Spreadsheet and choose to display a variety of graphical summaries of the coefficient (e.g., scatterplots with confidence intervals, various 3D bivariate distribution histograms, probability plots, etc.).
Brushing and outlier detection
The extensive brushing facilities in the scatterplots allow the user to select/deselect individual points in the plot and assess their effect on the regression line (or other fitted function lines).
Display formats of numbers
A variety of global display formats for correlations are supported. Significant correlation coefficients can be automatically highlighted, each cell of the Spreadsheet can be expanded to display n and p, or detailed results may be requested that include all descriptive statistics (pairwise means and standard deviations, B weights, intercepts, etc.). Like all other numerical results, correlation matrices are displayed in Spreadsheets offering the zoom option and interactivelycontrolled display formats (e.g., from +.4 to +.4131089276410193). Therefore, large matrices can be compressed to facilitate the visual search for coefficients which exceed a userspecified magnitude or significance level.
Scatterplot, scatterplot matrices, bygroup analyses
As in all output selection dialogs, numerous global graphics options are available to further study patterns of relationships between variables, e.g., 2D and 3D scatterplots designed to identify patterns of relations across subsets of cases or series of variables. Correlation matrices can be computed by grouping variables and visualised via categorised scatterplots. Also "breakdowns of correlation matrices" can be generated, displayed in queues of Spreadsheets, and saved as stacked correlation matrices. An entire correlation matrix can be summarised in a single graph via the Matrix scatterplot option. Large scatterplot matrices can then be reviewed interactively by zooming in on selected portions of the graph. Also, categorised scatterplot matrix plots can be generated. Alternatively, a multiplesubset scatterplot matrix plot can be created. Various other graphical methods can be used to visualise matrices of correlations in search of global patterns (e.g., contour plots, nonsmoothed surfaces, icons, etc.). All of these operations require only a few mouse clicks and various shortcuts are provided to simplify selections of analyses. A any number of Spreadsheets and graphs can be displayed simultaneously on the screen, making interactive exploratory analyses and comparisons very easy.
Basic Statistics from Results Spreadsheets (Tables)
Basic statistics (or any other statistical analysis) can be computed for results tables from previous analyses; for example, you could very quickly compute a table of means for 2000 variables, and next use this table as an input data file to further analyse the distribution of those means across the variables. Thus, basic statistics are available at any time during your analyses, and can be applied to any results spreadsheet.
Block Statistics
In addition to the detailed descriptive statistics that can be computed for every spreadsheet, you can also highlight blocks of numbers in any spreadsheet and produce basic descriptive statistics or graphs for the respective subset of numbers only. Statistical analysis by blocks can be performed by row or column. For example, you could compute a multiple line graph for a subset of variables across the different measures of central tendency. The block statistics facilities allow you to produce statistics and statistical graphs from values in arbitrarily selected blocks of values in the current data spreadsheet or output Spreadsheet.
Interactive Probability Calculator
A flexible, interactive Probability Calculator is accessible from all toolbars. It allows the user to visually explore distributions taking advantage of the flexible STATISTICA Smart MicroScrolls which allow the user to advance either the last significant digit or next to the last significant digit. Facilities are provided for generating customisable, compound graphs of distributions with requested cutoff areas.
tTests and Other Tests of Group Differences
Ttests for dependent and independent samples, as well as single samples can be computed. Multivariate Hotelling's T ^{2} tests are also available. Flexible options are provided to allow comparisons between variables and coded groups. As with all procedures, extensive diagnostics and graphics options are available from the results menus. For example, for the ttest for independent samples, options are provided to compute ttests with separate variance estimates, Levene and BrownForsythe tests for homogeneity of variance, various boxandwhisker plots, categorised histograms and probability plots as well as categorised scatterplots.
Frequency Tables, Cross Tabulation Tables, StubandBanner Tables, Multiple Response Analysis and Tables
Extensive facilities are provided to tabulate continuous, categorical, and multiple response variables or multiple dichotomies. A wide variety of options are available to control the layout and format of the tables. Frequency tables can also be computed based on userdefined logical selection conditions that assign cases to categories in the table. All tables can be extensively customised to produce publicationquality reports. The programme can display cumulative and relative frequencies, Logit and Probittransformed frequencies, normal expected frequencies as well as expected and residual frequencies in cross tabulations. Available statistical tests for cross tabulation tables include:

Pearson, MaximumLikelihood and Yatescorrected Chisquares

McNemar's Chisquare, the Fisher exact test (one and twotailed), Phi, and the tetrachoric r

Kendall's tau (a, b)

Gamma

Spearman r

Sommer's D

uncertainty coefficients
Graphs
Graphical options include simple, categorised, 3D histograms, crosssection histograms. Cascades of complex graphs can be interactively reviewed.
Multiple Regression Methods
The Multiple Regression module is a comprehensive implementation of linear regression techniques including simple, multiple, stepwise, hierarchical, nonlinear, Ridge regression, and weighted least squares models. Additional advanced methods are provided in the General Regression Models (GRM) module. The Multiple Regression module will calculate a comprehensive set of statistics and extended diagnostics including:

Regression table

Part and partial correlation matrices

Correlations and covariances for regression weights

The sweep matrix

The DurbinWatson d statistic

Mahalanobis and Cook's distances

Deleted residuals

Confidence intervals for predicted values
Predicted and residual values
The extensive residual and outlier analysis features a large selection of plots, including a variety of scatterplots, histograms, normal and halfnormal probability plots, detrended plots, partial correlation plots, different casewise residual and outlier plots and diagrams, and others. The scores for individual cases can be visualised via exploratory icon plots and other multidimensional graphs integrated directly with the results Spreadsheets. Residual and predicted scores can be appended to the current data file. A forecasting routine allows the user to perform whatif analyses and to interactively compute predicted scores based on userdefined values of predictors.
Bygroup analysis and related procedures
Extremely large regression designs can be analysed. An option is also included to perform multiple regression analyses broken down by one or more categorical variable. Additional addon procedures include a regression engine that supports models with thousands of variables, a Twostage Least Squares regression as well as BoxCox and BoxTidwell transformations with graphs.
Nonparametric Statistics
The Nonparametric Statistics module features a comprehensive selection of inferential and descriptive statistics including all common tests and some special application procedures. Available statistical procedures include:

WaldWolfowitz runs test

MannWhitney U test

KolmogorovSmirnov tests

Wilcoxon matched pairs test

KruskalWallis ANOVA by ranks

Median test

Sign test

Friedman ANOVA by ranks

Cochran Q test

McNemar test

Kendall coefficient of concordance

Kendall tau (b, c)

Spearman rank order R

Fisher's exact test

Chisquare tests

Vsquare statistic

Phi, Gamma

Sommer's d

contingency coefficients
All rank order tests can handle tied ranks and apply corrections for small n or tied ranks. The program can handle extremely large analysis designs. All tests are integrated with graphs that include various scatterplots, specialised boxandwhisker plots, line plots, histograms and many other 2D and 3D displays.
ANOVA/MANOVA
The ANOVA/MANOVA module can perform univariate and multivariate analysis of variance of factorial designs with or without one repeated measures variable. You can specify all designs in the most straightforward, functional terms of actual variables and levels. Even lessexperienced ANOVA users can analyse very complex designs with STATISTICA. ANOVA/MANOVA provides three alternative user interfaces for specifying designs:

A Design Wizard, that will take you stepbystep through the process of specifying a design

A simple dialogbased userinterface that will allow you to specify designs by selecting variables, codes, levels, and any design options from wellorganised dialogs,

A Syntax Editor for specifying designs and design options using keywords and a common design syntax.
The software will use, by default, the sigma restricted parameterisation for factorial designs, and apply the effective hypothesis approach when the design is unbalanced or incomplete. Type I, II, III, and IV hypotheses can also be computed, as can Type V and Type VI hypotheses.
Results statistics
The ANOVA/MANOVA module is not limited in any of its computational routines for reporting results. Results include:

Summary ANOVA tables

Univariate and multivariate results for repeated measures factors with more than 2 levels

The GreenhouseGeisser and HuynhFeldt adjustments

Plots of interactions

Detailed descriptive statistics

Detailed residual statistics

Planned and posthoc comparisons

Testing of custom hypotheses and custom error terms

Detailed diagnostic statistics and plots
Distribution Fitting
The Distribution Fitting options allow the user to compare the distribution of a variable with a wide variety of theoretical distributions. The fit can be evaluated via the Chisquare test or the KolmogorovSmirnov onesample test. The Lilliefors and ShapiroWilks' tests are also supported. In addition, the fit of a particular hypothesised distribution to the empirical distribution can be evaluated in customised histograms with overlaid selected functions. Also included in that module are options for automatically selecting and fitting the best distribution for the data, as well as options for general distribution fitting by moments (via Johnson and Pearson curves). Userdefined 2 and 3dimensional functions can also be plotted and overlaid on the graphs.
Integration and Connectivity
SharePoint (All Products)
The input into (and output from) STATISTICA 10 has now been integrated with the fastest growing standard for data exchange and integration – Microsoft SharePoint. STATISTICA documents can now be conveniently checked in and checked out of SharePoint from within the STATISTICA user interface. To the best of our knowledge, STATISTICA 10 is currently the only analytics or data mining application that offers this (seamlessly integrated) functionality.
Office 2010 (All Products)
STATISTICA imports directly native Office 2007 and 2010 files including the formatting information. This new technology has improved both the speed and fault tolerance of imports from Excel 2007 and 2010 to STATISTICA spreadsheets; the Excel 2007/2010 import/export now handles formatted cell text.
OLAP (All Products)
STATISTICA Query can now retrieve data from OLAP cube providers such as the Microsoft OLE DB Provider for Analysis Services or SAP Business Warehouse. MDX queries can be generated with a draganddrop environment, or the MDX code can be entered directly (currently offered in Beta release).
STATISTICA PI Connector (Addon Product)
It is now easier to install and manage the STATISTICA PI Connector in STATISTICA 10; the PI connector is distributed as part of version 10, and a separate installer is no longer necessary.
Data Visualization
Overview
The STATISTICA Graph display technology has been substantially upgraded to automatically detect and take advantage of the highperformance hardware acceleration, which is now available not only in the highend, but also in many midrange video display controllers available in both desktop and laptop computer workstations.
The resulting output is not only generated faster, but also supports more advanced smoothing and gradient display options. All STATISTICA Graphs have been enhanced with improved appearance, thanks to the new gradient/fill colors and smoother line display procedures (curves, surfaces).
Also, all STATISTICA Graph windows (both standalone and integrated into workbooks) now feature interactive graphics controls (a bar with sliders and other controls placed at the bottom of the graph window), which enable you to interactively adjust these new display features. The benefits include not only a vastly improved appearance of all graphs, but also new analytic and exploratory options, such as tools to reveal hidden trends by gradually desaturating dense displays and to rotate 3D graphs vertically and horizontally.
Interactive Scaling
You can now directly interact with the scaling on the graph by hovering the mouse pointer above the axis labels toward the end of the axis and pulling left or right to change the scaling. Interactive Scaling is a powerful graphical exploratory technique that enables you to reveal hidden trends by stretching or compressing the desired parts of the display.
Interactive Panning
You can now directly interact with the graph axis to pan to the right or the left by hovering the mouse pointer above the axis labels toward the center of the axis. Interactive Panning is a powerful graphical exploratory technique that assists you to explore trends hidden in the data.
Transparency
STATISTICA 10 supports transparency (interactively controlled with onscreen sliders) for controlling plot areas and desaturating overlapping markers (requires Windows Vista SP 2 or Windows 7). Transparency control is a powerful graphical exploratory technique that enables you to reveal trends hidden in the dense concentrations of data points (especially scatterplots and scatterplot matrices generated from extremely large data sets).
The goal is to achieve the optimal density level to uncover patterns obscured by a large number of random points (white noise) that create the “ink blot” effect. Additionally, making plot areas transparent allows portions of the plot to overlap while still being visible.
Reference Lines
Reference lines can be added to graphs much more easily in STATISTICA 10 through dedicated Reference Lines options, accessible in the Graph Options dialog.
Interactive Text Editing
Text can now be interactively edited onscreen (by simply clicking and typing in the edits), without a need to open the editor window. The graph text editor controls are still available and support the more advanced editing options.
Statistics
Design Simulation (All Products except STATISTICA Base)
STATISTICA 10 makes it easier to simulate data from a specific distribution with Design Simulation in the Distributions & Simulation module.
Simulated data can be highly useful in exploring the future and is becoming more accepted and adopted in different industries.
For example a company creates machines with precision parts. The knowledge about these machines and parts could be used to generate the data. Then the simulated data is analyzed for reliability.
Cox Proportional Hazards Models (All Products except STATISTICA Base)
A comprehensive and highly scalable implementation of the Cox Proportional Hazards Models (a powerful modeling technique for lifetime data) has been added to STATISTICA 10. Applications of this new module include:

analysis of survival data from patients in medical studies

customer churn analysis (loss of customer)

modeling and failure of mechanical parts (reliability)
The Cox Proportional Hazards Models module allows for flexible handling of censored data, categorical predictors, and designs that include interactions and/or nested effects. It uses model building techniques such as best subsets and stepwise regression. Deployment of the survival functions on new data is available with STATISTICA Rapid Deployment.
STATISTICA MSPC Online (STATISTICA MSPC Online Product)
In STATISTICA 10, the STATISTICA MSPC Online option makes it easier to deploy multivariate analysis (PCA, PLS) models to STATISTICA Enterprise for realtimeupdating, monitoring, and interactive drilldown from component scores, to contribution plots, and univariate charts.
Profit Chart (STATISTICA Data Miner)
Profit charts can now be created with STATISTICA’s Rapid Deployment of Predictive Models. The profit chart summarizes the costs and the estimated profit for the current model, and can be used in a wide variety of data mining application as one of the tools to evaluate the models.
ROC Curve (STATISTICA Data Miner)
ROC curves can now be created with STATISTICA’s Rapid Deployment of Predictive Models. It is another useful tool to evaluate the quality of models by visualizing the “true” positive versus the “false” positive rate. It is useful in many different fields such as medicine, quality control, and psychology. Side note: Interestingly, the ROC curve method has its roots in early days of radar technology, when it was used during World War II. Radar operators were evaluated on their ability to find “true” signals (airplanes) versus the “false” signals (birds). ROC curves are used today in data mining for similar reasons.
Text Mining (STATISTICA Text Miner)
In response to the recent trends in text mining, where enormously large data sets are being submitted for exploration and modeling, the main computational engine of STATISTICA Text Miner has been substantially redesigned and further optimized to improve its scalability and performance. The internal database handling procedures have been redesigned and the module can now handle extremely large data set very efficiently by extensive use of multithreading.
Java and C# Deployment (STATISTICA Data Miner – InPlace Database Deployment)
STATISTICA 10 provides two new deployment options: Java and C#. The latter also includes the ability to generate C# code in a form that can be directly incorporated into a SQL Server userdefined function, which can then be used in a storedprocedure to score the model directly inside the database. The Java code can be used the same way within Oracle userdefined functions. Note that this capability requires additional licensing. The main advantage of this deployment method is performance gains; the inside database deployment can be executed by an order of magnitude faster, compared to external processing.
Data Mining
Profit Chart (STATISTICA Data Miner)
Profit charts can now be created with STATISTICA’s Rapid Deployment of Predictive Models. The profit chart summarizes the costs and the estimated profit for the current model, and can be used in a wide variety of data mining application as one of the tools to evaluate the models.
ROC Curve (STATISTICA Data Miner)
ROC curves can now be created with STATISTICA’s Rapid Deployment of Predictive Models. It is another useful tool to evaluate the quality of models by visualizing the “true” positive versus the “false” positive rate. It is useful in many different fields such as medicine, quality control, and psychology. Side note: Interestingly, the ROC curve method has its roots in early days of radar technology, when it was used during World War II. Radar operators were evaluated on their ability to find “true” signals (airplanes) versus the “false” signals (birds). ROC curves are used today in data mining for similar reasons.
Text Mining (STATISTICA Text Miner)
In response to the recent trends in text mining, where enormously large data sets are being submitted for exploration and modeling, the main computational engine of STATISTICA Text Miner has been substantially redesigned and further optimized to improve its scalability and performance. The internal database handling procedures have been redesigned and the module can now handle extremely large data set very efficiently by extensive use of multithreading.
Java and C# Deployment (STATISTICA Data Miner – InPlace Database Deployment)
STATISTICA 10 provides two new deployment options: Java and C#. The latter also includes the ability to generate C# code in a form that can be directly incorporated into a SQL Server userdefined function, which can then be used in a storedprocedure to score the model directly inside the database. The Java code can be used the same way within Oracle userdefined functions. Note that this capability requires additional licensing. The main advantage of this deployment method is performance gains; the inside database deployment can be executed by an order of magnitude faster, compared to external processing.
Enterprise
Ribbon Bar
Application navigation in the STATISTICA 10 Enterprise Manager application is simpler and more efficient with the new ribbon bar.
Data Configurations
Data configurations are now available for selection from the STATISTICA System View, allowing the user to “explore” a data configuration from within the STATISTICA user interface, without needing to use Enterprise Manager.
Database Migration
The Database Migration tool is updated for the STATISTICA 10 Enterprise database schema, and is now available directly within STATISTICA Enterprise. It can be run by an administrator to copy configurations from one database to another database.
Publish Macros to STATISTICA Enterprise
STATISTICA 10 makes it easier to publish macros to STATISTICA Enterprise. This is a simpler method to create SVB Analysis Configurations, and works not only for SVB but also for R scripts. To access this new option, after creating the macro in STATISTICA, switch to the Enterprise tab and click Deploy Macro.
Enterprise Configuration Names
Enterprise Manager now allows more flexibility in defining the names of STATISTICA Enterprise configurations. Names now need to be unique only within the same System View folder.
STATISTICA Enterprise Server: Auto Updating Analysis Configuration Charts QC
Analysis Configurations that are set to autoupdate will now also autoupdate when run in a Web browser; the user can adjust the autoupdate interval from the browser, or initiate a manual update. The implementation uses the latest web technologies to update the image on the graph without needing to reload the web page (i.e., no “flashing” of the web page).
STATISTICA Enterprise Server: Quality Control Brushing
Quality Control Charts now support interactive brushing when run in a Web browser. The assignment of Causes, Actions, and Comments (as well as Include/Exclude) actions can now be accomplished through the web interface. The implementation uses the latest web technologies to update the image on the graph without needing to reload the web page (i.e., no “flashing” of the web page).
Other
STATISTICA MSPC Online (STATISTICA MSPC Online Product)
In STATISTICA 10, the STATISTICA MSPC Online option makes it easier to deploy multivariate analysis (PCA, PLS) models to STATISTICA Enterprise for realtimeupdating, monitoring, and interactive drilldown from component scores, to contribution plots, and univariate charts.
STATISTICA Web Data Entry (STATISTICA Web Data Entry Product)
STATISTICA Web Data Entry enables users to define data entry screens for entering data via Web browsers and storing/managing these data in the STATISTICA Enterprise database.
STATISTICA 10 Web Data Entry includes numerous enhancements, such as:

Easy to configure “required fields”

Improved navigation

Option to organize the fields into sections for easier data entry

Option to search historical samples using any Sample Label

Improved options for querying the data for use in analyses
STATISTICA Live Score (STATISTICA Live Score Product)
A new and improved version of STATISTICA Live Score is released with STATISTICA 10. STATISTICA Live Score is STATISTICA Server software within the STATISTICA Data Analysis and Data Mining Platform.
Data are aggregated and cleaned and models are trained and validated using the STATISTICA Data Miner software. Once the models are validated, they are deployed to the STATISTICA Live Score Server.
STATISTICA Live Score provides multithreaded, efficient, and platformindependent scoring of data from lineofbusiness applications. Some examples of the use of STATISTICA Live Score include:

Enabling credit scorecards to customer service applications (e.g., call center systems and Webbased applications)

Enabling customer segmentation, upsellcrosssell, and customer churn identification to customer service and marketing representatives

Enabling proactive fraud detection alerts to analysts
STATISTICA Scorecard (STATISTICA Scorecard Product)
STATISTICA Scorecard is a dedicated solution for development, evaluating, and monitoring Scorecards including steps for Feature Selection, Attribute Building, Scorecard Building, Cutoff Point Selection, Reject Inference, and Population Stability.
STATISTICA Object Model Examples (All Products)
Hundreds of STATISTICA Visual Basic examples have been added to the Help.