Principal Components Analysisoverview how to example data see also
Principal Components AnalysisStat > Multivariate > Principal Components
Use principal component analysis to help you to understand the underlying data structure and/or form a smaller number of uncorrelated variables (for example, to avoid multicollinearity in regression).
An overview of principal component analysis can be found in most books on multivariate analysis, such as [5].
Variables: Choose the columns containing the variables to be included in the analysis.
Number of components to compute: Enter the number of principal components to be extracted. If you do not specify the number of components and there are p variables selected, then p principal components will be extracted. If p is large, you may want just the first few.
Type of Matrix
Correlation: Choose to calculate the principal components using the correlation matrix. Use the correlation matrix if it makes sense to standardize variables (the usual choice when variables are measured by different scales).
Covariance: Choose to calculate the principal components using the covariance matrix. Use the covariance matrix if you do not wish to standardize variables.