The SCA-GSA module provides a wide range of general statistical capabilities from graphical to selected advanced analysis features. The SCA-GSA module provides features such as:

A key feature of SCA-GSA is its regression capabilities. In addition to standard regression analysis, models with serially correlated errors can be analyzed. These include lagged regressions with autoregressive or moving average noise terms. Other important enhancements to the standard regression model include:

Regression output is concise and easy to understand. You can control the amount of information you wish to view to include diagnostic tools such as:

The SCA-GSA module provides all standard analysis of variance measures, including:

In addition, the SCA-GSA module offers a capability not readily found in other statistical packages, Box-Cox transformation analysis. This powerful tool permits the user to incorporate the transformation of the response variable into an analysis. Analyses are often simplified and improved with this valuable addition. Lambda plots (plots of MSE or effects against transformation values) are also available for greater understanding of transformations.

The key part of any analysis is the beginning. Data should be displayed, and in a variety of ways. Basic descriptive measures should be calculated adnd examined. Only then will you best know how to proceed. The SCA-GSA module provides numerous data displays, including:

All basic descriptive statistics are available, as well as more advanced descriptive measures. These include:

Data are easily cross-classified in the SCA-GSA module, permitting an investigation of relationships between two or more variables. Capabilities encompass:

Most standard nonparametric tests are provided in the SCA-GSA module. These tests encompass:

Statistical capabilities within the SCA-GSA module are augmented with extensive mathematic and statistical functions and operators. These capabilities include: