The Principal Components & Classification Analysis (PCCA) module computes the principal components, and a wide range of the associated statistics. This module is designed to solve large-sized problems. Another unique feature of this module is that you can specify active and supplementary variables and cases. Active variables and cases are used in the derivation of the principal components; the supplementary variables and cases can then be projected onto the factor space computed from the active variables and cases. These facilities make the PCCA module a powerful tool for classification and data mining.

In addition, there is another module in Statistica also performing Principal Component Analysis under Nonlinear Iterative Partial Least Squares (NIPALS) modules.
Here is our online help about the PCA under NIPALS module.

Principal Components & Classification Analysis (PCCA)

Windows

To perform Principal Component and Classification analysis (PCCA):

1. Select "Statistics | Mult/Exploratory | Principal Components & Classification Analysis"



2. Under the "Quick" Tab of the analysis dialog, Click "Variables" to select the corresponding variables in each category



For explanation details of each variable category, click the "?" on top right of the analysis dialog to access our e-manual or click here. 

3. Enter the "Code for active cases"



4. Click "OK". Note: To choose different analyses type other than the default, select the "Advanced" tab for more options.



An example of applying this module can be found at e-manual or online help link Principal Components & Classification Analysis Example.
Check e-manual to Learn more about our Principal Components & Classification Analysis module