Statistica provides different packages of analytical techniques:

    * Base - Basic statistics options

The base version of the software includes core statistical functions: Descriptive Statistics, Breakdowns, and Exploratory Data Analysis; Correlations; Probability Calculator; T-Tests (and other tests of group differences); Frequency Tables, Crosstabulation Tables, Stub-and-Banner Tables, Multiple Response Analysis; Multiple Regression Methods; Nonparametric Statistics; Distribution Fitting


Additional modules are available for advanced features:

    * 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

    * Distribution and Simulation - Variance Components and Mixed Model ANOVA/ANCOVA; Survival/Failure Time Analysis; General Nonlinear Estimation (and Logit/Probit); Log-Linear 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; predictive analytics; Multivariate techniques and advanced linear/nonlinear regression models.

    * Power Calculations- Sample Size Calculations; Interval Estimation

    * Neural networks- neural networks; training algorithms

    * QC - Quality control techniques; design of experiments; Quality Control Charts; Process Capability Analysis; Weibull Analysis; Gage Repeatability & Reproducibility; Sampling Plans; Design of Experiments; Taguchi Designs

    * SAL- Sequence, Association, and Link Analysis

    * Data Miner - Data mining,

Additional techniques are available through Statistica integration with the free, open source R programming environment.