There is sometimes confusion between principal component analysis (PCA) and factor analysis (FA). Both methods have the aim of reducing the dimensionality of a vector of random variables. However, the most fundamental difference is that factor analysis explicitly specifies a model relating the observed variables to a smaller set of underlying unobservable factors. This assumed model may fit the data or not. In contrast PCA is just a data transformation method. Furthermore while Factor Analysis aims at explaining (covariances) or correlations, PCA concentrates on variances.

Citation

The E-Learning project SOGA-Py was developed at the Department of Earth Sciences by Annette Rudolph, Joachim Krois and Kai Hartmann. You can reach us via mail by soga[at]zedat.fu-berlin.de.

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You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: Rudolph, A., Krois, J., Hartmann, K. (2023): Statistics and Geodata Analysis using Python (SOGA-Py). Department of Earth Sciences, Freie Universitaet Berlin.