STATISTICA Advanced combines functionality from several different areas and includes all of the features included in the STATISTICA Basepackage. STATISTICA Advanced includes tools for linear/nonlinear models, multivariate exploratory techniques, and power analysis and interval estimation. Summaries of these areas and be found below and in depth descriptions of all the included modules can be found on the modules tab.
Advanced Linear/Nonlinear Models offers a wide array of the most advanced linear and nonlinear modeling tools on the market; supports continuous and categorical predictors, interactions, and hierarchical models; includes automatic model selection facilities as well as variance components, time series, and many other methods; and all analyses incorporate extensive, interactive graphical support and built-in complete Visual Basic scripting.
It features the following modules:
- Distribution and Simulation
- Variance Components and Mixed Model ANOVA/ANCOVA
- Survival/Failure Time Analysis
- Cox Proportional Hazard Model
- General Nonlinear Estimation (and Quick Logit/Probit Regression)
- Log-Linear Analysis of Frequency Tables
- Time Series Analysis/Forecasting
- Structural Equation Modeling/Path Analysis (SEPATH)
- General Linear Models (GLM)
- General Regression Models (GRM)
- Generalized Linear Models (GLZ)
- General Partial Least Squares Models(PLS)
Multivariate Exploratory Techniques offers a broad selection of exploratory techniques, from cluster analysis to advanced classification trees methods, with an endless array of interactive visualization tools for exploring relationships and patterns; built-in complete Visual Basic scripting.
Multivarite tools included:
- Cluster Analysis Techniques
- Factor Analysis
- Principal Components & Classification Analysis
- Canonical Correlation Analysis
- Reliability/Item Analysis
- Classification Trees
- Correspondence Analysis
- Multidimensional Scaling
- Discriminant Analysis
- General Discriminant Analysis Models (GDA)
Power Analysis and Interval Estimation is a powerful toolset for planning and analyzing your research and you can always be confident that you are using your resources most efficiently. Nothing is more disappointing than realizing that your research findings lack precision because your sample size was too small. On the other hand, using a sample size that is too large could be a significant waste of time and resources.
Power Analysis and Interval Estimation will help you find the ideal sample size and enrich your research with a variety of tools for estimating confidence intervals.
Power Analysis and Interval Estimation functions include:
- Power Calculation
- Sample Size Calculation
- Interval Estimation
- Probability Distributions
- List of Tests
- Example Application