Statistica provides point/click access to Machine Learning and cutting edge statistics, without coding in R.

Sale from A$3,990/user/year + gst
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Compare Statistica Editions

Point/click variable selection

Point/click variable selection

The variable (column) headers in the spreadsheet contain the variable names. Double-click on the first variable header – GENDER – to display its Variable specifications dialog and display format options.

Statistica Basic Statistics

Basic Statistics Start Panel

Point & click access to descriptive statistics, correlation, t-tests etc

Statistica Process Analysis

Process Capability Analysis

Consistent with DIN 55319 and ISO 21747

Statistica Data Miner

Statistica Data Miner (SDM) is a comprehensive system for predictive modeling that offers a wide variety of analytic techniques and model building, validation, and model deployment options.

Statistica Data Miner Recipe

Data Miner Recipes enable those without extensive experience with data mining tools to move very quickly from the definition of a problem to tangible and actionable results.

In this approach, you simply follow a recipe-like user interface to complete the necessary steps to move to a solution. In fact, most of these steps are entirely automated so that the only required input is to define the data and variables for the analyses, while the program automatically does the rest – determines learning and testing samples, performs feature selection, tries various data mining algorithms and methods, and evaluates results to select the best data mining model.

Feature List

Convenient window-based GUI with easy-to-use menus and dialogs

  • File import and export dialogs
  • Database import and export dialogs¹
  • Dialogs for data preparation, charting and statistical modeling
  • Interactive command-line with history recall
  • Manage objects with Object Explorer¹
  • Script file editor¹
  • Multiple data and graphics windows
  • Cut-and-paste to Word, PowerPoint and Excel¹
  • Integrated Excel spreadsheets¹
  • PowerPoint Wizard: quickly create slides from charts¹
  • Create custom toolbars, menus and dialogs¹
  • On-line help and manuals

Integrate with many data and graphics formats

  • ASCII: fixed format, comma-separated, and tab-delimited
  • Spreadsheets: Excel, Lotus 1-2-3, Quattro Pro
  • Application data: SAS 7/8/9, SPSS, Matlab, Minitab, Sigma Plot, Systat, STATA, Gauss, Epi Info and more
  • Database files: Paradox, dBase, Access, FoxPro
  • Financial data sources: LIM, Bloomberg, FAME
  • Native database clients: SQL Server¹, Oracle, Sybase, IBM DB2
  • ODBC interface to compliant databases
  • Read and write Spotfire binary and text files directly, helping to preserve important metadata when moving between applications.
  • Export graphics as PDF, PostScript, GIF, PNG, JPG, WMF, bitmap, TIFF and more
  • Customised, automated reports: XML reporting library speeds development of customized reports incorporating statistical tables and publication-quality graphics.

Create custom graphics

  • Interactive graphics system with a large & normalized palette for the creation of statistical charts to your exact specifications.
  • Scatterplots, histograms, pie charts, box plots, bar charts, dot charts, time series charts, 3-D wireframe charts, image plots and many more.
  • Brush and spin dynamic visualization
  • Programmatic control over colors, lines, axes, annotations and layout
  • Unique Trellis™ graphics – create multiple charts conditioned by levels of one or more variables
  • Create interactive, embedded web-based charts with S‑PLUS Graphlets™
  • Element-Specific Graph arguments for plots and command-line graphics

Hypothesis Tests and Confidence Intervals 

  • One-sample and two-sample t-test and Wilcoxon
  • Paired t-test
  • Correlation: Pearson, Kendall’s tau, Spearman’s rho
  • Goodness-of-Fit: Chi-square, Kolmogorov-Smirnov, Shapiro-Wilk
  • Rank tests: Kruskal-Wallis, Friedman
  • Proportions: exact Binomial test, Normal approximation
  • Contingency tables and tests for independence: Chi-square, Fisher, Mantel-Haenszel, McNemar


  • Basic linear regression
  • Polynomial regression
  • Model diagnostics
  • Prediction and confidence intervals
  • Stepwise selection of models
  • Parametric spline models
  • Constrained regression
  • Logistic regression
  • Generalized linear models

Analysis of Variance

  • Univariate and multivariate ANOVA
  • Flexible specification of variables, covariables, interactions, nesting, transformations
  • Automatic generation of dummy variables
  • Choice of contrasts
  • Type III sums of squares
  • Designed experiments: one-way, two-way, factorial, split-plot, unbalanced, fractional factorial designs, response surface methods, robust designs, taguchi methods and more
  • Variance component estimation
  • Multiple comparisons: Fisher, Tukey, Dunnett, Sidak, Bonferroni, Scheffé, simulation-based

Nonlinear Regression and Maximum Likelihood

  • Nonlinear regression
  • Nonlinear maximum likelihood
  • Quasi-likelihood
  • Constrained nonlinear regression

Nonparametric Regression

  • Generalized additive models (GAMs)
  • Smoothers: loess, super, kernel, spline
  • Projection Pursuit, ACE, and AVAS

Tree Models

  • Classification trees
  • Regression trees
  • Pruning, shrinking, and splitting
  • Scoring

Correlated Data Analysis

  • Longitudinal data and repeated measures analysis
  • Linear (LME), nonlinear (NLME), and generalized mixed effects (GLMM) models
  • Generalized Estimating Equations (GEE)
  • Biexponential, first-order compartment, four-parameter logistic models
  • User-defined correlation structures


  • Bootstrap
  • Jackknife

Multivariate Analysis

  • Canonical correlation
  • Discriminant analysis
  • Factor analysis
  • Multidimensional scaling
  • Principal components
  • Biplots

Cluster Analysis

  • K-means
  • Hierarchical clustering
  • Monothetic clustering
  • Model-based clustering
  • Crisp and fuzzy clustering
  • Divisive and agglomerative methods

Quality Control

  • Shewhart chart
  • Cusum chart
  • Charts based on xbar, s, np, p, c, u

Power and Sample Size

  • Normal mean
  • Binomial proportion

Survival Analysis

  • Kaplan-Meier curves
  • Cox proportional hazards models with mixed effects
  • Left, right, and interval censoring
  • Time-dependent covariates and strata
  • Multiple event models
  • Competing risk models
  • Frailty models
  • Parametric survival
  • Expected survival
  • Person years analysis
  • Aalen’s Additive Regression Model

Time Series Analysis 

  • Autocovariance, autocorrelation and partial autocorrelation
  • Smoothed periodograms
  • Box-Jenkins ARIMA models
  • Classical and robust AR
  • Long-memory models
  • Seasonal decompositions
  • Fourier transformations
  • Classical and robust smoothers and filters

Robust Statistics

  • Robust estimation and inferences
  • Robust MM regression
  • Robust GLM, ANOVA, covariance, principal components, and discriminant analysis
  • Least trimmed squares regression
  • Minimum absolute residual regression
  • Visually compare robust and traditional methods

Missing Data

  • Multiple imputation
  • Gaussian, logistic, and conditional Gaussian models

Date, Time, and Calendar Data

  • Univariate and multivariate time series
  • Aggregation, alignment, merging, and interpolation
  • Times and dates from milliseconds to millennia
  • Time zones with international daylight savings rules
  • Holidays and financial market closures
  • Custom time and date formats
  • Relative time, time sequence, and event objects
  • Powerful time-series charting
System Requirements
Processor2GHz or faster, Quad core  
(1 GHz, Dual core, minimum)
Hard Disk 500 MB of disk space to install
(If you are not installing on Drive C:\, an additional 50MB free disk space on Drive C:\ is required for the installation)
Administrator rights are required to install
Operating SystemMicrosoft® Windows 10
Microsoft® Windows 7 (32-bit and 64-bit)
Microsoft® Windows Vista® SP2 (32-bit & 64-bit)
Microsoft® Windows XP® SP3 (32-bit)
Parallels Desktop 15 for MacOS X 10.10 – 10.15