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The connection between a data mining tool and a spreadsheet application such as Excel is a really valuable feature. We benefit from the powerful of the first one, and the popularity and the easy to use of the second one. Many people use a spreadsheet in their data preparation phase. Recently, I have presented an add-in for the connection between R and Excel. In this document, I describe a similar tool for the SAS software. SAS is a popular tool, well-known of the statisticians. But the use of SAS is not really simple for the non-specialist people. We must know the syntax of the commands before to perform a statistical analysis. With the SAS add-in for Excel, some of the SAS drawbacks are alleviated: we do not need to load and organize the dataset into a bank; we do not need to know the command syntax to perform an analysis and set the associated parameters (we use a menu and dialog boxes instead); the results are automatically incorporated in a new sheet of an Excel workbook (the post

New features for the principal component analysis (PCA). PRINCIPAL COMPONENT ANALYSIS. Additional outputs for the component: Scree plot and variance explained cumulative curve; PCA Correlation Matrix - Some outputs are provided for the detection of the significant factors (Kaiser-Guttman, Karlis-Saporta-Spinaki, Legendre-Legendre broken-stick test); PCA Correlation Matrix - Bartlett's sphericity test is performed and the Kaiser's measure of sampling adequacy (MSA) is calculated; PCA Correlation Matrix - The correlation matrix and the partial correlations between each pair of variables controlling for all other variables (the negative anti-image correlation) are produced. PARALLEL ANALYSIS. The component calculates the distribution of eigenvalues for a set of randomly generated data. It proceeds by randomization. It applies to the principal components analysis and te multiple correspondence analysis. A factor is considered significant if its observed eigenvalue is greater than t