
PCA dimensions are also called axes or Factors. It is a projection method as it projects observations from a p-dimensional space with p variables to a k-dimensional space (where k < p) so as to conserve the maximum amount of information (information is measured here through the total variance of the dataset) from the initial dimensions. Principal Component Analysis is one of the most frequently used multivariate data analysis methods for dimensionality reduction.
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We also provide many free learning resources on the web, such as a tutorial on how to run PCA in XLSTAT as well as a guide to choose the best data mining or multivariate data analysis method according to your situation.



It is widely used in biostatistics, marketing, sociology, and many other fields. Principal Component Analysis ( PCA) is a powerful and popular multivariate analysis method that lets you investigate multidimensional datasets with quantitative variables.
