It determines a number of “components” (each a linear combination of the original variables) that explain much of the variation. PCA is a method used to analyze data with many variables.You can override this automatic decision as needed.
As you enter (or import) data, Prism defines each variable (column) as continuous, categorical or labels. It creates new variables (one less than the number of levels) to include in the model, using the reference level method. Prism knows how to fit models where a categorical variable has three or more levels (possible values). Multiple and logistic regression can now fit models where one or more of the predictor (independent) variables is entered as text and designated as a categorical variable. Each variable (column) is now identified as a continuous (numeric), categorical, or label variable. You can now enter text variables into multiple variable tables. No longer runs on 32-bit Windows, as 64-bit Windows has been standard for a decade. A data table can now have 1024 data sets and 512 sub-columns (up from 512 and 256). More data : Text variables and variable types (continuous, categorical, label) for multiple variables data tables. More data : Increased data table limits. Expanded analyses : Interpolation from Multiple Linear Regression. Expanded analyses : New options for Multiple t test analyses (paired, nonparametric, and more). New Graphs : Actual vs Predicted plot from nonlinear regression. New Graphs : Estimation plots automatically generated from t tests. New Graphs : Bubble plots and other multiple variables graphs from multiple variables data tables. More automation : Automatically add multiple comparison results to graphs ("Stars on Graph"). New analyses : Principal Component Analysis (PCA) and Principal Component Regression (PCR). 
If you have large data tables, you’ll also notice that Prism runs more quickly. You’ll just see more options in many places. If you are accustomed to Prism 8, you’ll find it easy to switch to Prism 9.
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