How do I run a Principal Component Analysis (PCA) with XLSTAT?
An Excel sheet containing both the data and the results for use in this tutorial can be downloaded by clicking here. The data are from the US Census Bureau and describe the changes in the population of 51 states between 2000 and 2001. The initial dataset has been transformed to rates per 1000 inhabitants, with the data for 2001 serving as the focus for the analysis. Our goal is to analyze the correlations between the variables and to find out if the changes in population in some states are very different from the ones in other states. This example is also used in our Hierarchical Clustering tutorial. PCA is a very useful method to analyze numerical data structured in a M observations / N variables table. It allows to: – Quickly visualize and analyze correlations between the N variables, – Visualize and analyze the M observations (initially described by the N variables) on a low dimensional map, the optimal view for a variability criterion, – Build a set of P uncorrelated factors (P The
